The Souris River Basin is an international basin in southeast Saskatchewan, north-central North Dakota, and southwest Manitoba. Sustained exceedances of water-quality objectives for total phosphorus, sodium, sulfate, total dissolved solids, and total iron have been reported since the late 1990s at the two binational sites on the Souris River (Souris River near Sherwood, North Dakota [U.S. Geological Survey station 05114000] and Souris River near Westhope, N. Dak. [U.S. Geological Survey station 05124000]). To understand conditions at the binational sites, it is important to understand water-quality changes on a basin-wide scale. Because streamflow is highly variable in the basin and changes in streamflow affect water-quality conditions, it is particularly important to use a trend-analysis method that accounts for changes in streamflow. Trends in water-quality concentrations can be affected by human-induced changes on the landscape or natural changes in land-runoff interactions that are driven by climate patterns and reflected by changes in streamflow (commonly referred to as “hydroclimatic variability”). In the primarily agricultural Souris River Basin, human-induced changes that are likely to affect trends are widespread changes in agricultural management such as fertilizer application, tilling practices, and crop types, as well as dam emplacement and artificial drainage. Around 1970, there was a long-term natural (hydroclimatic) change in the basin in which a significant transition from a dry climate state to a wet climate state resulted in higher streamflow in the basin. To assist the International Souris River Board in assessing current water-quality conditions in the Souris River Basin and exceedances of water-quality objectives at the binational sites, the U.S. Geological Survey, in cooperation with the International Joint Commission, completed a comprehensive analysis for selected ions, nutrients, and trace metals for many sites in the basin that included descriptive water-quality statistics, trend analysis using a trend method that considers interannual hydroclimatic variability, and an assessment of exceedances of the water-quality objectives for the binational sites.
Water-quality and streamflow or reservoir inflow or outflow data were compiled for 34 sites (30 stream sites and four reservoir sites) and 23 constituents with established water-quality objectives from 1970 to 2020 in the Souris River Basin and were used for descriptive statistics and water-quality trend analysis. Median total dissolved solids, sulfate, and sodium concentrations were low in the headwaters of the Souris River and some of the highest median concentrations were measured in the upper basin. At main-stem Souris River sites, all median sodium concentrations were greater than the binational water-quality objective. Median total phosphorus concentrations in the Souris River Basin were highest in the headwaters of the Souris River and all sites had median concentrations greater than the water-quality objective. Median total iron concentrations were highly variable across the basin, and for most main-stem sites, median concentrations were greater than or equal to the water-quality objective.
During the recent period (2009–19), the annual flow-averaged concentrations of total dissolved solids and sulfate increased for nearly all stream sites with most sites having mildly significant or significant increases. One-half of the sites had an annual flow-averaged geometric mean concentration greater than the total dissolved solids water-quality objective, and four sites had sulfate increases greater than 100 milligrams per liter. Trends in annual flow-averaged concentrations of sodium and chloride generally were small and nonsignificant. Most sites had concentrations greater than the sodium water-quality objective, whereas all sites had concentrations much less than the chloride water-quality objective. Annual flow-averaged geometric mean concentration of total phosphorus decreased for nearly all sites across the Souris River Basin, but all sites had concentrations greater than the total phosphorus water-quality objective for the entire period. Small and nonsignificant changes in annual flow-averaged geometric mean concentration of total iron were detected at all sites but the binational site at Sherwood, N. Dak., and by 2019 all sites had concentrations greater than the total iron water-quality objective. For the reservoir sites, during 2000–15, mostly significant increases for total dissolved solids, sulfate, and sodium were detected, whereas changes in total phosphorus and total iron were mixed.
During the historical period (1976–2019), large and consistent increases in total dissolved solids and sulfate have occurred since the late 1980s, with the largest increases and the most sites with mildly significant or significant increases generally occurring during the middle period (1988–2005). Large and significant or mildly significant increases in sodium concentrations occurred at eight of 10 sites in the middle period (1988–2005), and by the late period (2005–19) changes were small and nonsignificant. Similar to other basins in the region, such as the Red River of the North and Heart River, large and overall consistent increases since the late 1980s in total dissolved solids and sulfate in the Souris River Basin suggest that long-term natural (hydroclimatic) processes are large contributors to increases in the concentration of salts in streams and reservoirs associated with the onset of wetter conditions. The concurrent increases in sulfate and sodium concentrations at all sites during the middle period (1988–2005) suggest that sodium-sulfate evaporite dissolution may be a factor contributing to increases.
Total phosphorus concentrations oscillated between increasing and decreasing during the historical period, with concentrations increasing during the first trend period (1976–88) and decreasing in the fourth trend period (2009–19) to the lowest flow-averaged geometric mean concentration by 2019 for most sites. During the historical period, changes in total iron concentrations were mostly nonsignificant and generally small, and variability in total iron concentrations likely affected the ability to detect statistically significant changes in concentration.
The probability of exceeding the water-quality objective for total dissolved solids, sulfate, and sodium increased between 1976 and 2019 for the binational sites, especially for sulfate, which more than doubled for Souris River near Sherwood, N. Dak. and increased more than seven times for Souris River near Westhope, N. Dak. Total phosphorus and total iron concentrations for the binational sites were likely to exceed the water-quality objective for most of the year, but seasonal patterns of total phosphorus and total iron concentrations were different between the sites, suggesting that different factors may affect concentrations at different times of the year. For sodium, total phosphorus, and total iron, exceedance of the water-quality objective most of the time is not unexpected given that the flow-averaged geometric mean concentration for these three constituents for most sites across the basin are greater than the water-quality objective for most of the period. If natural processes are affecting total dissolved solids and sulfate concentrations, concentrations would be expected to vary with time, and as a result, extended periods of concentrations greater or less than the water-quality objective are likely to occur depending upon climatic conditions.
A better understanding of the state of water quality across the Souris River Basin is beneficial to understanding and interpreting water-quality conditions at the two Souris River binational sites. The most consistent spatial and temporal change observed for this study was large and consistent increases in sulfate and total dissolved solids among tributary and main-stem sites since the late 1980s. For sulfate and total dissolved solids, wetter climatic conditions combined with naturally occurring and abundant sources of sulfate likely contributed to sustained exceedances of water-quality objectives in recent decades, and extended periods of concentrations greater than or less than the water-quality objective are likely to occur depending on climatic conditions. For sodium, total iron, and total phosphorus, sustained exceedances of the current water-quality objective likely will continue because most sites across the basin had flow-averaged geometric mean concentrations greater than the water-quality objective; and during the 43-year period of analysis, regardless of climatic conditions, exceedances were consistently greater than the water-quality objective. Further investigation into the factors causing increasing sulfate concentrations and a better understanding of reservoir dynamics would enhance the understanding of changes in water-quality conditions in the Souris River Basin.
The basin-wide approach of this report provided an improved understanding of water-quality conditions in the Souris River Basin, and results can be used to inform the current water-quality objectives, inform potential changes to water management in the basin, and serve as a starting point for tracking future progress. Gaps in understanding of water-quality conditions can be closed through continued monitoring and further investigation into causes behind changes in water-quality conditions identified in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235084","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Nustad, R.A., and Tatge, W.S., 2023, Comprehensive water-quality trend analysis for selected sites and constituents in the International Souris River Basin, Saskatchewan and Manitoba, Canada, and North Dakota, United States, 1970–2020: U.S. Geological Survey Scientific Investigations Report 2023–5084, 83 p., https://doi.org/10.3133/sir20235084.","productDescription":"Report: viii, 83 p.; 4 Linked Tables; Data Release; Dataset","numberOfPages":"98","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-142196","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":419898,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5084/sir20235084_tables1.1-1.4.xlsx","text":"Appendix tables 1.1–1.4","size":"79.1 kB","linkFileType":{"id":3,"text":"xlsx"}},{"id":419895,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5084/sir20235084.pdf","text":"Report","size":"20.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5084"},{"id":419896,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5084/sir20235084.XML"},{"id":419899,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5084/sir20235084_tables1.1-1.4.zip","text":"Appendix tables 1.1–1.4","size":"14 kB","linkFileType":{"id":7,"text":"csv"}},{"id":419897,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5084/images"},{"id":419894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5084/coverthb.jpg"},{"id":419900,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TZAQ75","text":"USGS data release","linkHelpText":"Data and scripts used in water-quality trend analysis in the International Souris River Basin, Saskatchewan and Manitoba, Canada, and North Dakota, United States, 1970–2020"},{"id":501048,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115217.htm","linkFileType":{"id":5,"text":"html"}},{"id":419901,"rank":8,"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":419970,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235084/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"Canada, United States","state":"Manitoba, North Dakota, Saskatchewan","otherGeospatial":"International Souris River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105,\n 50.5\n ],\n [\n -105,\n 47.5\n ],\n [\n -99,\n 47.5\n ],\n [\n -99,\n 50.5\n ],\n [\n -105,\n 50.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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The Precipitation-Runoff Modeling System (PRMS) was used to develop and calibrate a streamflow and water balance model for the Red River Basin as part of the U.S. Geological Survey National Water Census, a research effort focused on developing innovative water accounting tools and conducting assessments of water use and availability at regional and national spatial scales. The PRMS is a deterministic model that simulates the effects of climate, land cover, and water use on watershed hydrology on the basis of physical processes and spatial attributes of the watershed. The model was used to estimate streamflow at daily and monthly temporal scales for the 1980–2016 period and to evaluate the impacts of natural and anthropogenic influences on streamflow and water budget components.
Sixty-three percent of streamgages were calibrated successfully for the monthly time step and 43 percent of streamgages were successfully calibrated for the daily time step. Some of the challenges of calibrating streamgages included estimating low amounts of streamflow in dry areas of the basin and accurately representing watershed characteristics related to evapotranspiration in the basin, among other factors. The model estimated streamflow with some accuracy for 42 percent and 29 percent of the 73 streamgages used to evaluate the model at monthly and daily time steps, respectively. Relative to no-water-use conditions, water use increased streamflow volumes (that is, return flow from reservoir releases) the most on the main stem of the Red River, the North Fork of the Red River, and the Ouachita River. Water withdrawal decreased streamflow volumes most in the Red River near the outlet of the basin and in Caney Creek. Streamflow volumes on the North Fork of the Red River changed most as a result of water use. The Red River Basin PRMS model provided estimates of streamflow that were limited in their accuracy by (1) the availability of accurate water-use data; (2) the coarse resolution of spatial parameters (such as those for impervious area or plant canopy), which leads to the homogenization of physical features in small watersheds in the model domain; and (3) the accuracy of spatial patterns of precipitation distribution across the model domain. Improvements in the quality and quantity of available water-use data and finer resolution spatial parameter and climate data could lead to the development of better-informed models in the future that are capable of making more accurate estimates of streamflow, because they are more representative of physical and hydrologic conditions in the Red River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225105","issn":"2328-0328","programNote":"Water Availability and Use Science Program","usgsCitation":"Roland, V.L., II, 2023, Application of the Precipitation-Runoff Modeling System (PRMS) to simulate the streamflows and water balance of the Red River Basin, 1980–2016: U.S. Geological Survey Scientific Investigations Report 2022–5105, 37 p., https://doi.org/10.3133/sir20225105.","productDescription":"Report: viii, 37 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-091577","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":417622,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5105/coverthb.jpg"},{"id":500454,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114764.htm","linkFileType":{"id":5,"text":"html"}},{"id":417790,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZI5IVX","text":"USGS data release—Model input and output from Precipitation Runoff Modeling System (PRMS) simulation of the Red River Basin 1981–2016"},{"id":417789,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5105/images/"},{"id":417921,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225105/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5105 HTML"},{"id":417787,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5105/sir20225105.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2022-5105 XML"},{"id":417786,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5105/sir20225105.pdf","size":"16.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5105"}],"country":"United States","state":"Arkansas, Louisiana, Texas, Oklahoma","otherGeospatial":"Red River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.48949921760368,\n 36.13692959102481\n ],\n [\n -103.48949921760368,\n 31.242144043970583\n ],\n [\n -89.91622130795812,\n 31.242144043970583\n ],\n [\n -89.91622130795812,\n 36.13692959102481\n ],\n [\n -103.48949921760368,\n 36.13692959102481\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"For more information about this publication, contact
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
https://www.usgs.gov/centers/lmg-water/
In 2007, the U.S. Geological Survey partnered with Fairfax County, Virginia, to establish a long-term water-resources monitoring program to evaluate the hydrology, water quality, and ecology of Fairfax County streams and the watershed-scale effects of management practices. Fairfax County uses a variety of management practices, policies, and programs to protect and restore its water resources, but the effects of such strategies are not well understood. This report used streamflow, water-quality, and ecological monitoring data collected from 20 Fairfax County watersheds from 2007 through 2018 to assess the effects of management practices, landscape factors, and climatic conditions on observed nutrient, sediment, salinity, and benthic-macroinvertebrate community responses.
Urbanization, climatic variability, and an increase in management practices occurred within Fairfax County during the study period. Impervious cover, housing units, wastewater infrastructure, and (or) stormwater infrastructure increased in most study watersheds. Climatic conditions varied among study years; countywide estimates of average-annual air temperature differed by about 3 degrees Celsius, and total precipitation ranged from about 34 to 63 inches per year. The effects of the management practices, implemented to reduce nitrogen, phosphorus, and (or) sediment loads, are considered in this study. These management practices primarily consist of stormwater retrofits and stream restorations; however, stream restorations account for most of the financial investment and expected load reductions. Management practices were implemented in half of the study watersheds, and most practices were installed and reductions credited late in the study period.
Changes in hydrologic response during storm events were evaluated over the study period because many management practices that were implemented were designed to achieve nutrient and sediment reductions by slowing or intercepting runoff. The average number and length of storm events was mostly unchanged throughout the monitoring network. Four watersheds with 10 years of streamflow data showed a mixture of trends in stormflow peak, volume, and rate-of-change. Event-mean nutrient and sediment concentrations from these watersheds were evaluated during storm events and generally showed increases in total phosphorus (TP) and suspended sediment and reductions or no changes in total nitrogen (TN).
Landscape inputs of nitrogen and phosphorus and the percentage of inputs delivered to streams were estimated for the study watersheds. Estimated phosphorus from fertilizer and nitrogen from atmospheric deposition represented large nutrient inputs in most watersheds; amounts of other nonpoint sources varied based on land use. Estimated nitrogen inputs declined throughout Fairfax County and in most study watersheds from 2008 through 2018; in comparison, phosphorus input changes were relatively small. Most nonpoint-nutrient inputs were retained on the landscape and did not reach streams, with slightly more nitrogen retention than phosphorus, on average. Retention rates were lower for years with more precipitation and streamflow. After adjusting for streamflow, TN and TP loads were generally higher for years with more nutrient inputs. Calculated as a function of flow-adjusted loads, TP retention declined at most stations from 2009 through 2018, in comparison, TN retention was relatively unchanged.
Landscape and climatic conditions affected spatial differences and changes in Fairfax County stream conditions from 2009 through 2018. TN concentrations were higher and increases over time were larger in watersheds with elevated septic-system density. TP concentrations were higher in watersheds with more turfgrass; concentrations were lower, but had larger increases over time, in watersheds with deeper soils. Suspended-sediment concentrations were higher in watersheds with greater stream densities. Specific conductance was higher in watersheds with more developed land use and shallower soils. Benthic-macroinvertebrate index of biotic integrity (IBI) scores were lower in watersheds with high road density and had larger increases over time in bigger, more developed watersheds. Annual variability in TN and TP concentrations and benthic-macroinvertebrate IBI scores was affected by precipitation; annual variability in suspended sediment concentrations and specific conductance was affected by air temperature.
After accounting for influences from landscape and climatic conditions, expected management-practice effects were not consistently observed in monitored stream responses. These effects were assessed by comparing expected management-practice load reductions with the timing, direction, and magnitude of changes in storm-event hydrology, nutrient and sediment loads, median-annual water-quality conditions, and benthic-macroinvertebrate IBI scores. An important consideration for future investigations of management-practice effects is how to control for water-quality and ecological variability caused by geologic properties, the urban environment, precipitation, and (or) air temperature. The interpretation of management-practice effects in this report was likely influenced by a combination of factors, including (1) the amount, timing, and location of management-practice implementation; (2) unmeasured landscape and climatic factors; (3) uncertain management-practice expectations; (4) hydrologic variability; and (5) analytical assumptions. Through continued data-collection efforts, particularly after management practices have been completed, many of these factors may become less influential in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235027","isbn":"978-1-4113-4516-4","collaboration":"Prepared in cooperation with Fairfax County, Virginia","usgsCitation":"Webber, J.S., Chanat, J.G., Porter, A.J., and Jastram, J.D., 2023, Evaluating drivers of hydrology, water quality, and benthic macroinvertebrates in streams of Fairfax County, Virginia, 2007–18: U.S. Geological Survey Scientific Investigations Report 2023–5027, 198 p., https://doi.org/10.3133/sir20235027.","productDescription":"Report: xv, 198 p.; Data Release","numberOfPages":"198","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-139637","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":417148,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FW7KLH","text":"USGS data release","linkHelpText":"Climate, landscape, and water-quality metrics 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Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Groundwater in the karst groundwater system at Area B of Fort Detrick in Frederick County, Maryland, is contaminated with chlorinated solvents from the past disposal of laboratory wastes. In cooperation with U.S. Army Environmental Command and U.S. Army Garrison Fort Detrick, the U.S. Geological Survey performed a 3-year study to refine the conceptual model of groundwater flow in and around Area B of Fort Detrick at the site- to regional-scale. The investigation was designed to review the geologic setting, assess the temporal variability of the hydrologic system, evaluate the potential for interbasin groundwater flow, determine the degree of vertical connectivity of the aquifer, characterize the sources and timing of groundwater recharge, and identify if dyes from previous tracer tests continue to drain from the aquifer. This study established a continuous hydrologic monitoring network of 12 water level gages, 2 streamgages, a precipitation gage, and in situ fluorometric monitoring. A water budget analysis was performed using hydrologic monitoring data and a soil-water balance model constructed for the study. In this study each individual water budget term is calculated using available data or through modeling, and a water budget residual term is calculated. If the water budget residual term is small relative to the uncertainty of the underlying data, then an additional import or export of water (in other words, interbasin transfer) is not needed to fully describe the hydrologic system. Groundwater and spring samples from 20 locations were collected in a 2019 synoptic geochemical sampling event and analyzed for a suite of analytes that included groundwater age tracer constituents.
The karst groundwater system was found to be highly responsive to hydrologic events, with strong water level and stream base flow responses to individual storm events and a historic wet period in 2017 and 2018. The water budget analysis included historic flooding in May 2018, though more typical hydrologic patterns were observed in 2019 and 2020. During most evaluated intervals, the water budget residual was less than the estimated uncertainty on the residual for the two Carroll Creek watersheds, which suggested no substantial net interbasin flow occurs from these watersheds. The watershed difference area, a region that includes Area B, had a significant negative water budget residual, which may be the result of a net interbasin import of groundwater or the result of focused groundwater recharge not simulated by the soil-water balance model. Geochemical analysis and groundwater age dating reveals shallow groundwater (approximately less than [<] 150 feet deep) appears to be relatively young (approximately <30 years) and to be recharged in the vicinity of Area B. In the deep groundwater sampled in this study (approximately greater than [>] 150 feet deep), older groundwater from a differing recharge source, based on stable isotopes and noble gas analyses, is observed and interpreted to represent less direct connectivity to the surface and increased proportions of water recharged to the north and (or) west of Area B. A clustering analysis to reveal groupings within the suite of geochemical data was used to define seven groups. The groupings generally show that wells in similar depths and lateral aquifer positions generally cluster together, with some exceptions. Although limited by suspended sediments, the in situ fluorometric monitoring at springs did not detect any dye leaving the system above the limit of detection for the method. Dye was only detected above the limit of detection in one well, which was used as an injection well during a previous dye tracer test.
The results of this study support and refine the conceptual site model of groundwater hydrology at Area B. The geologic and geophysical log review in this study agrees with prior assessments of physical controls on groundwater flow. A literature review of mid-Atlantic karst studies identified similar controls reported in these environments. The additional characterization of hydrologic responsiveness in this study suggests that hydrologic conditions and events are important considerations when interpreting potentiometric surfaces and contaminant trends over time and highlights the importance of continuous hydrologic monitoring. There is evidence to suggest that either intense focused groundwater recharge occurs in the vicinity of Area B or net along-valley groundwater interbasin flow from the upper study watershed enters the lower watershed and discharges to Carroll Creek. Geochemical analyses also suggest that water recharged from Catoctin Mountain and the elevated areas to the north and (or) west of the site may be present in the older and deeper Area B groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225054","collaboration":"Prepared in cooperation with U.S. Army Environmental Command and U.S. Army Garrison, Fort Detrick","usgsCitation":"Goodling, P.J., Fleming, B.J., Solder, J., Soroka, A., and Raffensperger, J., 2023, Hydrogeologic characterization of Area B, Fort Detrick, Maryland: U.S. Geological Survey Scientific Investigations Report 2022–5054, 128 p., https://doi.org/10.3133/sir20225054.","productDescription":"Report: xiv, 128 p.; 2 Data Releases","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124092","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":435349,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DUFZY7","text":"USGS data release","linkHelpText":"Supporting Datasets for Hydrogeological Characterization of Ft. Detrick Area B, Maryland"},{"id":500936,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114709.htm","linkFileType":{"id":5,"text":"html"}},{"id":416517,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GTTX8Q","text":"USGS data release","linkHelpText":"Soil water balance model developed for Maryland and Pennsylvania"},{"id":416516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AYWBXU","text":"USGS data release","linkHelpText":"Supporting datasets for hydrogeological characterization of Area B, Fort Detrick, Maryland"},{"id":416515,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.pdf","text":"Report","size":"51.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5054"},{"id":416514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5054/coverthb.jpg"},{"id":416562,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225054/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5054"},{"id":416564,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5054/images/"},{"id":416563,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.XML"}],"country":"United States","state":"Maryland","otherGeospatial":"Fort Detrick","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.4693386578843,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.458154924593\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
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Chromium concentrations in rock and aquifer material in Hinkley and Water Valleys in the Mojave Desert, 80 miles northeast of Los Angeles, California, are generally low compared to the average chromium concentration of 185 milligrams per kilogram (mg/kg) in the average bulk continental crust. Chromium concentrations in felsic, coarse-textured “Mojave-type” deposits, composed of Mojave River stream (alluvium) and lake-margin (beach) deposits sourced from the Mojave River, are as low as 5 mg/kg, with a median concentration of 23 mg/kg in aquifer materials adjacent to the screened intervals of sampled wells. The most abundant chromium-containing mineral within aquifer materials in Hinkley and Water Valleys is magnetite. Magnetite is resistant to weathering, and about 90 percent of chromium remains within unweathered mineral grains. However, chromium-containing hornblende diorite and basalt are present in surrounding uplands, and chromium-containing actinolite is present within some aquifer materials.
Although geologic abundance of chromium is clearly important, hexavalent chromium, Cr(VI), concentrations in alkaline oxic groundwater are related to additional factors. Hexavalent chromium concentrations in groundwater are influenced by a combination of processes including (1) mineralogy and the weathering rates of chromium-containing minerals; (2) texture of aquifer deposits; (3) accumulation of chromium weathered from minerals within surface coatings on mineral grains; (4) oxidation of accumulated Cr(III) to Cr(VI) in the presence of manganese oxides (Mn oxides), including the abundance and oxidation states of those Mn oxides; (5) pH-dependent desorption of chromium from coatings on the surfaces of mineral grains into groundwater during appropriate aqueous geochemical conditions; and (6) age (time since recharge) of groundwater. The pH of groundwater increases with groundwater age (time since recharge) as a result of silicate weathering, and desorption of Cr(VI) from aquifer deposits increases with increasing pH as long as groundwater remains oxic. In the absence of the detailed geologic, geochemical, and hydrologic data collected as part of this study, pH-dependent sorption, evaluated as the Cr(VI) occurrence probability at the measured pH, is an effective indicator of natural or anthropogenic Cr(VI).
The Pacific Gas and Electric Company (PG&E) Hinkley compressor station is used to compress natural gas as it is transported through a pipeline from Texas to California. Between 1952 and 1964, cooling water containing Cr(VI) was discharged to unlined ponds and released into groundwater in unconsolidated aquifers. The extent of groundwater containing evidence of at least some anthropogenic Cr(VI) was 5.5 square miles (mi2) and was estimated using a summative scale incorporating geologic, geochemical, and hydrologic data collected from more than 100 wells between March 2015 and November 2017. The summative-scale Cr(VI) plume extent is larger than the 2.2 mi2 extent of the October–December 2015 (Q4 2015) regulatory Cr(VI) plume but is smaller than the 8.3 mi2 maximum mapped extent of Cr(VI) greater than the interim regulatory Cr(VI) background concentration of 3.1 micrograms per liter (μg/L). The summative-scale Cr(VI) plume is within felsic, low-chromium aquifer material deposited by the Mojave River described as Mojave-type deposits and is within the area covered by the PG&E monitoring well network.
Background Cr(VI) concentrations were calculated using the computer program ProUCL 5.1 as the upper 95-percent tolerance limit, UTL95, using data from wells outside the summative-scale Cr(VI) plume extent collected between April 2017 and March 2018. The overall UTL95 for undifferentiated, unconsolidated deposits in the eastern and western subareas and the northern subarea upgradient of the Mount General fault in Hinkley Valley was 3.8 μg/L; this value is similar to the overall UTL95 value of 3.9 μg/L calculated for Mojave-type deposits in Hinkley and Water Valleys, and is similar to the maximum Cr(VI) concentration of older groundwater in contact with Mojave-type deposits of 3.6 μg/L.
In most cases the overall UTL95 value may be an acceptable Cr(VI) background value near the Cr(VI) plume margin; however, UTL95 values for the various subareas in Hinkley and Water Valleys provide greater resolution of Cr(VI) background that may be important for some purposes. The UTL95 values for undifferentiated, unconsolidated deposits in the eastern, western, and northern subareas upgradient of the Mount General fault were 2.8, 3.8, and 4.8 μg/L, respectively. The UTL95 value of 2.8 μg/L for wells screened in undifferentiated, unconsolidated deposits in the eastern subarea is important for plume management because the Hinkley compressor station and most of the summative-scale Cr(VI) plume are within the eastern subarea. A UTL95 value of 2.3 μg/L was calculated for Mojave-type deposits downgradient from the Hinkley compressor station. This value represents Cr(VI) concentrations that may have been present in that part of the aquifer had Cr(VI) not been released from the Hinkley compressor station, and it reflects coarser textured deposits in this area and the proximity of those deposits to recharge areas along the Mojave River that results in younger (post-1952), less alkaline groundwater than in wells farther downgradient. This value may be a suitable metric for Cr(VI) cleanup goals within the Cr(VI) plume after regulatory updates. A separate UTL95 value of 5.8 μg/L was calculated for mudflat/playa deposits and older groundwater near Mount General in the eastern subarea. The UTL95 values calculated for undifferentiated, unconsolidated deposits in the northern subarea downgradient from the Mount General fault and in Water Valley, including lacustrine (lake) deposits and material eroded from basalt and Miocene deposits, were 9.0 and 6.4 μg/L, respectively.
Hexavalent chromium concentrations in more than 70 domestic wells sampled between January 27 and 31, 2016, ranged from less than the study reporting level of 0.1–4.0 μg/L, with a median concentration of 1.2 μg/L. Hexavalent chromium concentrations in water from domestic wells did not exceed UTL95 values within subareas where the wells were located. Water from 47 percent of domestic wells sampled between January 27 and 31, 2016, had arsenic, uranium, or nitrate concentrations above a maximum contaminant level.
Anthropogenic Cr(VI) within groundwater downgradient from the Hinkley compressor station is treated by PG&E using bioremediation by adding ethanol as a reductant within a volume of aquifer known as the in situ reactive zone (IRZ). Laboratory microcosm studies showed that Cr(VI) is rapidly reduced to Cr(III) with additions of ethanol. Reduced Cr(III) is sorbed and is sequestered into crystalline iron and manganese oxides on the surfaces of mineral grains within the microcosms during a period of several months. Trivalent chromium was reoxidized back to Cr(VI) within 2 weeks of return to oxic (oxygen present) conditions within the microcosms. As much as 10 percent of added Cr was oxidized to Cr(VI) in microcosms prepared using recent Mojave River aquifer material, and as much as 20 percent of added Cr was oxidized to Cr(VI) in microcosms prepared using older Mojave River aquifer material. Less Cr(VI) (less than 3 percent of Cr added before reduction) was released to the aqueous phase, and this release occurred following longer time periods of oxygen exposure. Sequestration of chromium with manganese oxides during reduction facilitates reoxidation of Cr(III) to Cr(VI) under oxic conditions. Future maintenance of anoxic (oxygen absent) conditions would ensure continued sequestration of chromium as Cr(III) within IRZ treated portions of the Cr(VI) plume.
Although Cr(VI) within the summative-scale Cr(VI) plume may have an anthropogenic history associated with releases from the Hinkley compressor station, Cr(VI) concentrations less than the UTL95 values for the various subareas may not require regulatory attention. The regulatory Cr(VI) plume can be updated using the UTL95 values calculated as part of this study. The updated regulatory Cr(VI) plume extent would lie within the summative-scale Cr(VI) plume extent. The authority to establish regulatory Cr(VI) background values, clean-up goals, and future site management practices resides with the Lahontan Regional Water Quality Control Board.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1885J","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board","usgsCitation":"Izbicki, J.A., Groover, K.D., Seymour, W.A., Miller, D.M., Warden, J.G., and Miller, L.G., 2023, Summary and conclusions, Chapter J of Natural and anthropogenic (human-made) hexavalent chromium Cr(VI), in groundwater near a mapped plume, Hinkley, California: U.S. Geological Survey Professional Paper 1885-J, 55 p., https://doi.org/10.3133/pp1885J.","productDescription":"Report: x, 55 p.; 5 Data Releases","numberOfPages":"55","additionalOnlineFiles":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":416312,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CU0EH3","text":"Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California","description":"Groover, K.D., and Izbicki, J.A., 2018, Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9CU0EH3."},{"id":416309,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1885/j/images"},{"id":416308,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1885/j/pp1885j.xml"},{"id":416307,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1885/j/pp1885j.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416306,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1885/j/covrthb.jpg"},{"id":417468,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231043","text":"Open-File Report 2023-1043","linkHelpText":"- Natural and Anthropogenic Hexavalent Chromium, Cr(VI), in Groundwater near a Mapped Plume, Hinkley, California"},{"id":416315,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HUPMG0","text":"Grain size, mineralogic, and trace-element data from field samples near Hinkley, California","description":"Morrison, J.M., Benzel, W.M., Holm-Denoma, C.S., and Bala, S., 2018, Grain size, mineralogic, and trace-element data from field samples near Hinkley, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9HUPMG0."},{"id":416314,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U8C82V","text":"Aqueous and solid phase chemistry of sequestration and re-oxidation of chromium in experimental microcosms with sand and sediment from Hinkley, CA","description":"Miller, L.G., Bobb, C., Bennett, S., and Baesman, S.M., 2020, Aqueous and solid phase chemistry of sequestration and re-oxidation of chromium in experimental microcosms with sand and sediment from Hinkley, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P9U8C82V."},{"id":416313,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BUXAX1","text":"Hydrologic data in Hinkley and Water Valleys, San Bernardino County, California, 2015–2018","description":"Groover, K.D., Izbicki, J.A., Larsen, J.D., Dick, M.C., Nawikas, J., and Kohel, C.A., 2021, Hydrologic data in Hinkley and Water Valleys, San Bernardino County, California, 2015–2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9BUXAX1."},{"id":416311,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ENBLGY","text":"Optical Petrography, Bulk Chemistry, Microscale Mineralogy/Chemistry, and Bulk/Micron-Scale Solid-Phase Speciation of Natural and Synthetic Solid Phases Used in Chromium Sequestration and Re-oxidation Experiments with Sand and Sediment from Hinkley, CA","description":"Foster, A.L., Wright, E.G., Bobb, C., Choy, D., and Miller, L.G., 2023, Optical petrography, bulk chemistry, micro-scale mineralogy/chemistry, and bulk/micro-scale speciation of solid phases used in chromium sequestration and re-oxidation experiments with sand and sediment from Hinkley, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9ENBLGY."}],"country":"United States","state":"California","city":"Hinkley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116,\n 35.25\n ],\n [\n -117.75,\n 35.25\n ],\n [\n -117.75,\n 34.25\n ],\n [\n -116,\n 34.25\n ],\n [\n -116,\n 35.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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Process-based, large-scale (e.g., conterminous United States [CONUS]) hydrologic models have struggled to achieve reliable streamflow drought performance in arid regions and for low-flow periods. Deep learning has recently seen broad implementation in streamflow prediction and forecasting research projects throughout the world with performance often equaling or exceeding that of process-based models. Deep learning models are a possible approach to increase the accuracy of streamflow drought predictions and to expand the spatial coverage of river locations with available streamflow drought forecasts.
As part of a multi-component Data-Driven Drought Prediction project, the U.S. Geological Survey is developing and testing deep learning models for streamflow drought forecasting. In this work, we present preliminary results of a deep learning model capable of predicting streamflow drought occurrence at ungaged locations for the Colorado River Basin (CRB). A long short-term memory (LSTM) neural network model was trained using 40 years (1980-2020) of daily streamflow data from 425 streamgages within and surrounding the CRB using static watershed attributes as well as meteorological and remotely sensed dynamic forcing inputs. Model tests were performed to evaluate model accuracy for now-casting streamflow drought conditions at ungaged locations and for forecasting drought conditions at lead times ranging from 0 to 14 days. Nearly all model configurations showed behavioral performance for predicting daily streamflow percentiles. Comparisons of LSTM model performance for predicting drought using fixed drought thresholds (calculated over all days and years) and variable drought thresholds (unique threshold calculated for each day of the year) identify differences in model skill between locations with implications for model design.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","publisher":"SEDHYD","usgsCitation":"Hamshaw, S.D., Goodling, P.J., Hafen, K., Hammond, J., McShane, R., Sando, R., Shastry, A.R., Simeone, C.E., Watkins, D., White, E., and Wieczorek, M., 2023, Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models, in Proceedings of SEDHYD 2023, St. Louis, MO, May 8-12, 2023, 15 p.","productDescription":"15 p.","ipdsId":"IP-151973","costCenters":[{"id":227,"text":"Earth Surface Dynamics Program","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":419560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419548,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s181.html"}],"country":"United States","otherGeospatial":"Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.60045372623478,\n 31.258330936607123\n ],\n [\n -110.59254302741863,\n 31.054063075754513\n ],\n [\n -108.59802952983767,\n 31.359920476818175\n ],\n [\n -107.77329728728904,\n 32.667013027627036\n ],\n [\n -105.33291815667258,\n 38.21996621737378\n ],\n [\n -105.64401667449579,\n 40.61584869706027\n ],\n [\n -108.25906974720414,\n 42.992213339755665\n ],\n [\n -110.41906640637575,\n 43.12924273929244\n ],\n [\n -111.27275669412631,\n 41.39663030950132\n ],\n [\n -112.47810183593663,\n 38.504465490675386\n ],\n [\n -113.09970923969854,\n 37.353465042204334\n ],\n [\n -114.3929667988645,\n 37.49906345159148\n ],\n [\n -114.6266795454161,\n 38.107130943367025\n ],\n [\n -115.48463166591264,\n 39.43479765478551\n ],\n [\n -115.68353845853977,\n 37.41554450267796\n ],\n [\n -115.1115276804997,\n 33.75507968847421\n ],\n [\n -115.55059550089697,\n 31.937316150454635\n ],\n [\n -114.60045372623478,\n 31.258330936607123\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hamshaw, Scott Douglas 0000-0002-0583-4237","orcid":"https://orcid.org/0000-0002-0583-4237","contributorId":305601,"corporation":false,"usgs":true,"family":"Hamshaw","given":"Scott","email":"","middleInitial":"Douglas","affiliations":[{"id":37778,"text":"WMA - 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2023 - Status and trends of total nitrogen and total phosphorus concentrations, loads, and yields in streams of Mississippi, water years 2008–18","interactions":[],"lastModifiedDate":"2026-02-24T18:36:28.127594","indexId":"sir20235003","displayToPublicDate":"2023-02-24T07:30:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5003","displayTitle":"Status and Trends of Total Nitrogen and Total Phosphorus Concentrations, Loads, and Yields in Streams of Mississippi, Water Years 2008–18","title":"Status and trends of total nitrogen and total phosphorus concentrations, loads, and yields in streams of Mississippi, water years 2008–18","docAbstract":"To assess the status and trends of conditions of surface waters throughout Mississippi, the U.S. Geological Survey, in cooperation with the Mississippi Department of Environmental Quality (MDEQ), summarized concentrations and estimated loads, yields, trends, and spatial and temporal patterns of total nitrogen (TN) and total phosphorus (TP) at 20 stream sites in MDEQ’s ambient water-quality monitoring network and 2 stream sites in the U.S. Geological Survey’s National Water-Quality Assessment Project’s monitoring network.
Comparison of streamflow at the time of water-quality sample collection to flow-duration curves for each site showed that samples were relatively evenly spread over a wide range of flows, indicating that load estimations were representative of a wide range of flows. Relation of streamflow to concentrations of TN and TP varied among sites and land use. Sites with high agriculture land use in the drainage basin tended to have a positive correlation between streamflow and concentration, suggesting influence of event-driven nonpoint-source runoff. Sites near urban (developed) areas tended to have a negative correlation between streamflow and concentration, suggesting chronic point-source influences during low-flow conditions. Sites with high forest land use and lower agriculture and urban (developed) land use showed little to no association between streamflow and concentration.
Seasonal distributions of concentrations of TN and TP also corresponded closely with variations in land use. Sites near urban (developed) land had the highest concentrations in late summer and fall, sites with a high percentage of agricultural land had the highest concentrations in the spring, and sites that were primarily forested or with little developed land did not exhibit substantial changes in concentration across seasons.
Eight sites had statistical likelihoods for upward trends of TN loads, and seven sites had statistical likelihoods for downward trends. Trends in TN loads at six sites were considered “about as likely as not,” meaning that a site has an equal chance of having an upward or downward trend. Trend results of mean annual flow-normalized loads of TP for the period of analysis (2008–18) showed that 16 sites had upward trends, 3 sites had downward trends, and 2 sites were considered “about as likely as not.”
Results from our study were compared to results from existing regional models to assess accuracy of predictions at a local scale. Comparisons of yields predicted from 2012 regional-scale SPAtially Referenced Regressions on Watershed attributes (SPARROW) to results from this study showed the 2012 SPARROW-predicted estimates varied in consistency with results from this study. The 2012 SPARROW-prediction model underestimated TN yields, more often and by a slightly larger degree, more than it overestimated TN yields. The 2012 SPARROW-predicted model tended to underestimate yields at study sites with higher yields. All four sites in the predominantly agricultural area of northwest Mississippi, locally known as the Mississippi Delta, were underestimated by 2012 SPARROW. For TP, yield comparisons at sites with lower yields were consistent, yields at sites with midrange yields tended to be overestimated by SPARROW, and yields at sites with high yields tended to be underestimated by SPARROW. TP yields at four sites in the Mississippi Delta were underestimated by the 2012 SPARROW-predicted model.
Results of select sites from our study were also compared to other published load estimates from an earlier time period to evaluate possible trends. Comparison of TN yields at four sites and TP yields at three sites from the study-derived estimates to estimates made from data spanning 1993–2004 showed decreasing TN yields at all four sites and decreasing TP yields at two of three sites, with increasing yields of TP at the Yazoo River lower site. Also, a third comparison of the TN and TP yields of the Yazoo River lower site of this study to estimates made from data spanning 1996–97 showed decreasing TN yields but similar TP yields. This suggests that TN yields may have decreased over the last 20–30 years, but TP yields remain constant or are increasing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235003","issn":"ISSN 2328-0328","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality","usgsCitation":"Hicks, M.B., Crain, A.S., and Segrest, N.G., 2023, Status and trends of total nitrogen and total phosphorus concentrations, loads, and yields in streams of Mississippi, water years 2008–18: U.S. Geological Survey Scientific Investigations Report 2023–5003, 77 p., https://doi.org/10.3133/sir20235003.","productDescription":"Report: x, 77 p.; Data Release; Dataset","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-130707","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":413300,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VV2U70","text":"USGS data release","linkHelpText":"Datasets of streamflow, nutrient concentrations, loads 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\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
During water years 2013–18, the U.S. Geological Survey National Water-Quality Assessment Project sampled the National Water Quality Network for Rivers and Streams year-round and reported on 221 pesticides at 72 sites across the United States. Pesticides are difficult to measure, their concentrations often represent discrete snapshots in time, and capturing peak concentrations is expensive. Three types of regression models were developed to estimate concentrations for two selected pesticides at each of six National Water Quality Network for Rivers and Streams sites. The regression models used continuously measured streamflow and water-quality properties (differing combinations of pH, specific conductance, turbidity, and water temperature); discrete water-quality samples analyzed for atrazine, azoxystrobin, bentazon, bromacil, imidacloprid, simazine, and triclopyr; and time as an additional explanatory variable for seasonality.
The modeling approaches included (1) a standard regression that included surrogates (differing combinations of pH, specific conductance, turbidity, and water temperature) and periodic functions (sine-cosine) of pesticide application use as predictor variables; (2) the seasonal wave with flow adjustment model that included a seasonal component and flow anomalies but excluded surrogates; and (3) the seasonal wave with flow adjustment model that included a seasonal component, flow anomalies, and surrogates. Models were evaluated using three measures of model performance: generalized coefficient of determination (generalized R2), Akaike’s Information Criteria, and scale (the estimated standard deviation of the tobit regression error term). Because of low observation numbers, results from this study can be considered a pilot effort with the possibility that some models are overfit.
In all cases, estimated pesticide concentrations modeled with base SEAWAVE-Q were better than the standard surrogate regression models; all 39 generalized R2 values increased by 3–56 percent (median of 25 percent) when compared to the standard surrogate regression models, and all Akaike’s Information Criteria and scale values decreased. The addition of surrogate variables such as pH, specific conductance, turbidity, and water temperature to the base SEAWAVE-Q model to improve estimates of pesticide concentrations resulted in only modest improvements; generalized R2 values increased by only 0–10 percent (median of 3 percent). In some instances, combinations of the surrogates produced more appreciative improvements in model results, but in those instances, we hypothesize that the surrogates correlated with some unknown measure that directly relates to pesticide transport.
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\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278-1996
New and previously published stratigraphic data define Holocene to present sediment storage time scales for Mid-Atlantic river corridors. Empirical distributions of deposit ages and thicknesses were randomly sampled to create synthetic age-depth records. Deposits predating European settlement accumulated at a (median) rate of 0.06 cm yr−1, range from ∼18,000 to 225 yr old, and represent 39% (median) of the total accumulation. Sediments deposited from 1750 to 1950 (“legacy sediments”) accumulated at a (median) rate of 0.39 cm yr−1 and comprise 47% (median) of the total, while “modern sediments” (1950−present) represent 11% of the total and accumulated at a (median) rate of 0.25 cm yr−1. Synthetic stratigraphic sequences, recast as age distributions for the presettlement period, in 1900 A.D., and at present, reflect rapid postsettlement alluviation, with enhanced preservation of younger sediments related to postsettlement watershed disturbance. An averaged present age distribution for vertically accreted sediment has modal, median, and mean ages of 190, 230, and 630 yr, reflecting the predominance of stored legacy sediments and the influence of relatively few, much older early Holocene deposits. The present age distribution, if represented by an exponential approximation (mean age ∼300 yr), and naively assumed to represent steady-state conditions, implies median sediment travel times on the order of centuries for travel distances greater than ∼100 km. The percentage of sediment reaching the watershed outlet in 30 yr (a reasonable time horizon to achieve watershed restoration efficacy) is ∼60% for a distance of 50 km, but this decreases to <20% for distances greater than 200 km. Age distributions, evaluated through time, not only encapsulate the history of sediment storage, but they also provide data for calibrating watershed-scale sediment-routing models over geological time scales.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B36282.1","usgsCitation":"Pizzuto, J., Skalak, K., Benthem, A.J., Mahan, S.A., Sherif, M., and Pearson, A., 2023, Spatially averaged stratigraphic data to inform watershed sediment routing: An example from the Mid-Atlantic United States: GSA Bulletin, v. 135, no. 1-2, p. 249-270, https://doi.org/10.1130/B36282.1.","productDescription":"22 p.","startPage":"249","endPage":"270","ipdsId":"IP-132540","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":445529,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/b36282.1","text":"Publisher Index Page"},{"id":406955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.57373046875,\n 36.57142382346277\n ],\n [\n -75.069580078125,\n 36.57142382346277\n ],\n [\n -75.069580078125,\n 40.01078714046552\n ],\n [\n -80.57373046875,\n 40.01078714046552\n ],\n [\n -80.57373046875,\n 36.57142382346277\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"135","issue":"1-2","noUsgsAuthors":false,"publicationDate":"2022-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pizzuto, James","contributorId":207115,"corporation":false,"usgs":false,"family":"Pizzuto","given":"James","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":852245,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Skalak, Katherine 0000-0003-4122-1240 kskalak@usgs.gov","orcid":"https://orcid.org/0000-0003-4122-1240","contributorId":3990,"corporation":false,"usgs":true,"family":"Skalak","given":"Katherine","email":"kskalak@usgs.gov","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":852246,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benthem, Adam J. 0000-0003-2372-0281","orcid":"https://orcid.org/0000-0003-2372-0281","contributorId":220000,"corporation":false,"usgs":true,"family":"Benthem","given":"Adam","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852247,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":852248,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sherif, Mahmoud 0000-0002-6504-0439","orcid":"https://orcid.org/0000-0002-6504-0439","contributorId":296698,"corporation":false,"usgs":false,"family":"Sherif","given":"Mahmoud","email":"","affiliations":[{"id":64145,"text":"Tanta University","active":true,"usgs":false}],"preferred":false,"id":852249,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearson, Adam 0000-0002-6719-9750","orcid":"https://orcid.org/0000-0002-6719-9750","contributorId":296699,"corporation":false,"usgs":false,"family":"Pearson","given":"Adam","email":"","affiliations":[{"id":64146,"text":"SUNY, Postdam","active":true,"usgs":false}],"preferred":false,"id":852250,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238905,"text":"sir20225114 - 2022 - BFS—A non-linear, state-space model for baseflow separation and prediction","interactions":[],"lastModifiedDate":"2022-12-19T11:54:05.582352","indexId":"sir20225114","displayToPublicDate":"2022-12-16T12:26:01","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5114","displayTitle":"BFS—A Non-Linear, State-Space Model for Baseflow Separation and Prediction","title":"BFS—A non-linear, state-space model for baseflow separation and prediction","docAbstract":"Streamflow in rivers can be separated into a relatively steady component, or baseflow, that represents reliably available surface water and more dynamic components of runoff that typically represent a large fraction of total streamflow. A spatially aggregated numerical time-series model was developed to separate the baseflow component of a streamflow time-series using a state-space framework in which baseflow is a non-linear function of upstream storage, an unmeasured state variable. The state-space framework allows forecasting of baseflow for periods with no rainfall or snowmelt and estimation of residence times in contrast to other hydrograph separation models. The use of a non-linear relation between baseflow and storage maintains model performance over a wide range of time scales but will only provide reliable predictions for periods when the rate of streamflow recession as a fraction of streamflow decreases over time.
The baseflow separation model, BFS, is implemented as set of functions in the statistical computing language R. BFS is run using the main function, bf_sep, which reads model input (a time series of streamflow), calculates the baseflow component of streamflow, writes model output to a file, and returns an error to the user to facilitate automated calibration. The function, bf_sep, has six arguments, which a user must enter: a numerical vector with the time series of measured streamflow volume for each time step; a character string, timestep, that has a value of either “daily” or “hourly” indicating the time step; a character string, error_basis, indicating which simulated streamflow components are used for error calculations; a six-element numeric vector, flow, with parameters characterizing streamflow; a six-element vector, basin_char, with parameters characterizing the geometry of stream basin and reservoirs; and a six-element vector, gw_hyd, with hydraulic parameters. The function bf_sep calls a series of other functions to calculate surface and base reservoir storage and fluxes.
Calibration of a non-linear model for baseflow recession must confront three issues. First, baseflow is a component of streamflow, so it is always less than or equal to streamflow but there is no independent standard for the baseflow component of streamflow. Second, optimization routines can converge on a set of model parameters that result in relatively steady but minimal baseflow that does not exceed streamflow, Q, but has a limited dynamic range. Third, the power function used to generate non-linear first-order baseflow recession (dQ/dt)/Q ≠ constant) may only be sensitive to parameters over a limited range of values, which may not be found by optimization routines.
To address these issues, BFS calculates error as the mean of weighted differences between measured streamflow and either simulated baseflow or the sum of simulated baseflow and surface flow as a fraction of measured streamflow. The difference for each time step is weighted by an exponential function of the length of recession for each time step ranging from 0 for periods when streamflow increases and approaching 1 for long recessional periods. The weight is set to 1 for any time step when simulated streamflow exceeds measured streamflow. Error calculation incorporates limited precision of streamflow measurements.
A four-step calibration process was developed to find a set of viable parameters that maximize the baseflow component within the constraints of the conceptual model (a first-order recession rate that decreases during dry periods). BFS was calibrated at 13,208 U.S. Geological Survey streamgages with available daily streamflow records for at least 300 days from water years 1981 to 2020. The total simulated baseflow component as a fraction of streamflow (BFF) was generally less than the baseflow index (BFI) for 8,368 streamgages where BFF and BFI were available. The median difference was BFF–BFI = 0.11. Large differences were most common in the Interior West where streamflow in many rivers is regulated and is generated predominantly by snowmelt. The baseflow separation model generally allocates less streamflow to baseflow than graphical hydrograph separation in snowmelt rivers.
BFS can be used to forecast streamflow during dry periods by using a time series of real-time streamflow with values of Not Available (NA), appended to the time-series to represent missing (future) streamflow values. The forecast skill of BFS was evaluated in terms of difference between simulated baseflow and measured streamflow as a fraction of measured streamflow on the days of the annual maximum recession period at 5,916 of the sites with at least 10 years of record. The median annual error was less than 50 percent at one-half of the sites and generally improved for drier years with longer recession periods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225114","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the Washington State Department of Ecology","usgsCitation":"Konrad, C.P., 2022, BFS—A non-linear, state-space model for baseflow separation and prediction: U.S. Geological Survey Scientific Investigations Report 2022–5114, 24 p., https://doi.org/10.3133/sir20225114.","productDescription":"Report: vii, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-122969","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":410595,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5114/coverthb.jpg"},{"id":410596,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5114/sir20225114.pdf","text":"Report","size":"18.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5114"},{"id":410598,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AIPHEP","text":"USGS data release","description":"USGS data release","linkHelpText":"Non-linear baseflow separation model with parameters and results (ver. 2.0, October 2022)"},{"id":410599,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5114/images"},{"id":410600,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5114/sir20225114.XML"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
We develop an empirical, spatially continuous model for the single-station within-event (ϕSS) component of earthquake ground motion variability in the Los Angeles area. ϕSS represents event-to-event variability in site response or remaining variability due to path effects not captured by ground motion models. Site-specific values of ϕSS at permanent seismic network stations were estimated during our previous work [1]. Here we first fit a model to ϕSS conditioned on the time-averaged shear wave velocity in the upper 30 m (VS30). We observe that stations on soft soil have larger average variability in site response than those on rock, especially at short periods. We use regression kriging to spatially interpolate ϕSS to an even grid spacing, using the VS30-scaling as the background model [2]. This improves on previous work that used ordinary kriging for interpolation [1]. We find that ϕSS ranges from about 0.1 to 0.4 natural log units in our study area, representing variations in site response at single locations of a factor of 1.1 up to a factor of 1.5. There is both greater variability and more coherency in the variability for short-period site response than for long periods. We recommend using these ϕSS models with the site response models of [1] for applications where quantification of variability is needed, such as probabilistic seismic hazard analyses.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings from the 12th national conference on earthquake engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"12th National Conference on Earthquake Engineering","conferenceDate":"Jun 27 - Jul 1, 2022","conferenceLocation":"Salt Lake City, UT","language":"English","publisher":"Earthquake Engineering Research Institute","usgsCitation":"Parker, G.A., Baltay Sundstrom, A.S., and Thompson, E.M., 2022, Spatially continuous models of aleatory variability in seismic site response for southern California, in Proceedings from the 12th national conference on earthquake engineering, Salt Lake City, UT, Jun 27 - Jul 1, 2022, 5 p.","productDescription":"5 p.","ipdsId":"IP-134370","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":411890,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":411887,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://12ncee.org/program/proceedings"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117,\n 35\n ],\n [\n -119,\n 35\n ],\n [\n -119,\n 33\n ],\n [\n -117,\n 33\n ],\n [\n -117,\n 35\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Parker, Grace Alexandra 0000-0002-9445-2571","orcid":"https://orcid.org/0000-0002-9445-2571","contributorId":237091,"corporation":false,"usgs":true,"family":"Parker","given":"Grace","email":"","middleInitial":"Alexandra","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":848815,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":848816,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":848817,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236510,"text":"70236510 - 2022 - Geologic characterization and depositional history of the Uteland Butte member, Green River Formation, southwestern Uinta Basin, Utah","interactions":[],"lastModifiedDate":"2022-09-09T13:39:43.82889","indexId":"70236510","displayToPublicDate":"2022-09-01T08:33:14","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geologic characterization and depositional history of the Uteland Butte member, Green River Formation, southwestern Uinta Basin, Utah","docAbstract":"The 15- to 65-m-thick informal Uteland Butte member of the Eocene Green River Formation represents the first widespread transgression of Lake Uinta in the Uinta Basin, Utah. This study assesses the spatial and temporal variation of Uteland Butte member deposits along a 40-km transect in the southwestern margin of the Uinta Basin using detailed measured sections, organic and inorganic geochemical data, and outcrop gamma ray logs. Fourteen lithofacies are identified, which comprise seven facies associations linked to with lacustrine, palustrine, and deltaic depositional settings. Facies associations are traceable laterally across the study area, where five 4- to 12-m-thick depositional cycles are identified. Each shallowing upwards cycle is defined by a >1.5-m-thick basal package of organic-rich, argillaceous laminated mudstone, and is capped by thick packages of bedded carbonate. In the far western study area (Kyune Creek Canyon), thick deposits of organic-rich mudstone are present and represent the most distal outcrop section; time-equivalent strata in the eastern study area (Minnie Maud Creek Canyon) are relatively organic lean with higher silt and clay content, interpreted to represent proximal lake margin deposits influenced by a nearby delta. The outcrop belt is correlated to more distal cores and well logs across the western Uinta Basin. Similar lithological and petrophysical patterns across the western Uinta Basin are used to subdivide stratigraphy into nine laterally contiguous sub-units based on nomenclature from the oil-producing area of the central basin (from base to top: lower Uteland Butte, D Bench, D Shale, C Bench, C Shale, B Bench, B Shale, A Bench, and A Shale). Siliciclastic clay-rich and carbonaterich intervals are correlated across the region and indicate distinct siliciclastic- and carbonate-dominated lake phases during Uteland Butte member deposition. Climate is interpreted to be the dominant driver of these claycarbonate cycles, in which relatively humid periods resulted in increased fluvially derived siliciclastic sediment into the basin (clay-rich periods), and arid periods resulted in evaporative conditions with decreased fluvial sediment input that favor carbonate accumulation. Climatically driven depositional cycles within the Uteland Butte member reflect, to a smaller degree, the larger scale climatically driven depositional cycles observed at the member- and formation levels of Paleocene and Eocene Uinta Basin stratigraphy. Importantly, the Uteland Butte member clay-carbonate cycles showcase how relatively small-scale climate shifts can impact basin-scale lacustrine deposition.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Utah Geological Association","doi":"10.31711/ugap.v50i.106","usgsCitation":"Gall, R.D., Birdwell, J.E., Brinkerhoff, R., and Vanden Berg, M.D., 2022, Geologic characterization and depositional history of the Uteland Butte member, Green River Formation, southwestern Uinta Basin, Utah, chap. of The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record, v. 50, p. 37-62, https://doi.org/10.31711/ugap.v50i.106.","productDescription":"26 p.","startPage":"37","endPage":"62","ipdsId":"IP-127906","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":446587,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31711/ugap.v50i.106","text":"Publisher Index Page"},{"id":435703,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X66RQ4","text":"USGS data release","linkHelpText":"Geochemical and spectroscopic data on outcrop samples from the informal Uteland Butte member of the Eocene Green River Formation in Uinta Basin, Utah"},{"id":406449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Uinta Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.786376953125,\n 39.757879992021756\n ],\n [\n -109.281005859375,\n 40.283716270542584\n ],\n [\n -109.49523925781249,\n 40.56389453066509\n ],\n [\n -110.2972412109375,\n 40.543026009955014\n ],\n [\n -110.8740234375,\n 40.35073056591789\n ],\n [\n -110.687255859375,\n 40.057052221322\n ],\n [\n -110.28076171875,\n 39.85915479295669\n ],\n [\n -109.8797607421875,\n 39.73253798438173\n ],\n [\n -109.786376953125,\n 39.757879992021756\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationDate":"2022-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gall, Ryan D.","contributorId":296324,"corporation":false,"usgs":false,"family":"Gall","given":"Ryan","email":"","middleInitial":"D.","affiliations":[{"id":17626,"text":"Utah Geological Survey","active":true,"usgs":false}],"preferred":false,"id":851281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":851282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brinkerhoff, Riley","contributorId":296326,"corporation":false,"usgs":false,"family":"Brinkerhoff","given":"Riley","email":"","affiliations":[{"id":64017,"text":"Wasatch Energy Management","active":true,"usgs":false}],"preferred":false,"id":851283,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vanden Berg, Michael D.","contributorId":177609,"corporation":false,"usgs":false,"family":"Vanden Berg","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":851284,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236495,"text":"70236495 - 2022 - Geoelectric constraints on the Precambrian assembly and architecture of southern Laurentia","interactions":[],"lastModifiedDate":"2022-09-09T13:17:21.375872","indexId":"70236495","displayToPublicDate":"2022-08-27T08:10:01","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geoelectric constraints on the Precambrian assembly and architecture of southern Laurentia","docAbstract":"Using images from an updated and expanded three-dimensional electrical conductivity synthesis model for the contiguous United States (CONUS), we highlight the key continent-scale geoelectric structures that are associated with the Precambrian assembly of southern Laurentia. Conductivity anomalies are associated with the Trans-Hudson orogen, the Penokean suture, the ca. 1.8–1.7 Ga Cheyenne belt and Spirit Lake tectonic zone, and the Grenville suture zone; the geophysical characteristics of these structures indicate that the associated accretionary events involved the closure of ancient ocean basins along discrete, large-scale structures. In contrast, we observe no large-scale conductivity anomalies through the portion of southern Laurentia that is generally viewed as composed of late Paleoproterozoic–early Mesoproterozoic accretionary crust. The lack of through-going conductors places constraints on the structure, petrology, and geodynamic history of crustal growth in southern Laurentia during that time period. Overall, our model highlights the enigmatic nature of the concealed Precambrian basement of much of southern Laurentia, as it in some places supports and in other places challenges prevailing models of Laurentian assembly. The revised CONUS electrical conductivity model thus provides important constraints for testing new models of Precambrian tectonism in this region.
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MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars","title":"Geologic map of MTM −10022 and −15022 quadrangles, Morava Valles and Margaritifer basin, Mars","docAbstract":"The landscape in Mars Transverse Mercator (MTM) −10022 and −15022 quadrangles (lat −7.5° N. to −17.5° N. between long 335° E. and 340° E.) in Margaritifer Terra preserves a record of sedimentary and alluvial deposits, volcanic and tectonic structures, and erosional landforms that record a long and complex geologic and geomorphic history. MTM −10022 and −15022 quadrangles primarily encompass Morava Valles, the terminus of the Samara-Himera and Paraná-Loire valley networks, the broad catchment informally named Margaritifer basin, and Margaritifer Chaos. Morava Valles is the lowermost reach of the northward draining mesoscale outflow system that consists of Uzboi Vallis, Ladon Valles, and Morava Valles, was sourced from flow out of Argyre basin, and incises across and between the ancient Ladon and Holden impact basins. The broad-scale topography and surface relief within the map, including the topographic low occupied by Margaritifer basin, were largely shaped during the Noachian by the formation of the Holden, Ladon, and Ares impact basins and the Chryse trough. Multiple processes modified the ancient surface until the Late Noachian and resulted in the formation of the terra unit that forms the widely exposed surface. Later resurfacing associated with likely sedimentary and volcanic processes modified predominantly lower elevation surfaces and basins during the Late Noachian into at least the Hesperian. Sedimentary processes during the Late Noachian were dominated by fluvial incision of the Samara-Himera and Paraná-Loire valley networks and discharge related to the dissection of Morava Valles that drained Ladon basin. The history of geomorphic activity within Margaritifer basin was more complex and was likely dominated by the evolution of Morava Valles relative to the formation of the valley networks. The floor of Margaritifer basin preserves likely lacustrine plains related to sedimentation in water ponded during early discharge from Morava Valles, which were later embayed by volcanic plains. Crater densities and cross-cutting relations indicate Margaritifer basin evolved over a relatively short period of geologic time. The timing of the last drainage out of Morava Valles is not well constrained but could have occurred during the Hesperian. Structural collapse and the formation of the Margaritifer Chaos and other chaotic terrain formed by the release of subsurface water that may have been related to volcanic activity along the southern margin of Margaritifer basin. Final geomorphic events within the map region include the formation of Late Hesperian to perhaps Amazonian alluvial fans within some craters and isolated mass wasting on steep slopes. A final, variable veneer associated with locally occurring impacts and redistribution of fine-grained material by eolian processes resulted in the landscape observed today.
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Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Groundwater levels have declined since the 1940s in the Wailuku area of central Maui, Hawai‘i, on the eastern flank of West Maui volcano, mainly in response to increased groundwater withdrawals. Available data since the 1980s also indicate a thinning of the freshwater lens and an increase in chloride concentrations of pumped water from production wells. These trends, combined with projected increases in demand for groundwater in central Maui, have led to concerns over groundwater availability and have highlighted a need to improve general understanding of the hydrologic effects of proposed groundwater withdrawals in the Waihe‘e, ‘Īao, and Waikapū areas of central Maui.
A numerical groundwater model was constructed to simulate the flow and salinity of groundwater in central Maui. The model simulates the effects of changes in groundwater withdrawals and recharge on water levels, freshwater-lens thicknesses, and chloride concentrations of pumped water from production wells. The model incorporates updated water-budget estimates of groundwater recharge from infiltration and direct recharge, seepage in stream channels, and inflow from inland areas. Mean annual groundwater recharge from infiltration and direct recharge was estimated using a daily water-budget model and the most current data, including the distributions of monthly rainfall and potential evapotranspiration, for the study area for nine historical periods from 1926 through 2012: 1926–69, 1970–79, 1980–84, 1985–89, 1990–94, 1995–99, 2000–04, 2005–09, and 2010–12. The water-budget model also estimated groundwater recharge based on one hypothetical scenario that used 1980–2010 rainfall and 2017 land cover. For the nine historical periods, estimated recharge from infiltration and direct recharge within the area of the groundwater model ranged from 30.4 million gallons per day (Mgal/d) during 2010–12 to 98.7 Mgal/d during 1926–69. Variability in recharge during these periods mainly reflects changes in rainfall and irrigation over time. Between 2010 and 2014, streamflow restoration in previously diverted streams resulted in an estimated increase in recharge from seepage in stream channels of about 12.5 Mgal/d. Average groundwater inflow of about 39.6 Mgal/d from inland, dike-intruded areas to the main area of interest was estimated from an existing island-wide numerical groundwater-flow model, which is at a larger scale and incorporates a greater number of simplifying assumptions.
The numerical groundwater model developed for this study was calibrated to 1926–2012 transient water levels, vertical salinity profiles, and chloride concentrations of water pumped by production wells in the study area. The model was then used to evaluate one future recharge and six selected withdrawal scenarios, developed in consultation with the Maui Department of Water Supply, in terms of long-term changes in water level and 50-percent ocean-water salinity surface. The groundwater model was also used to simulate the future salinity of water withdrawn by existing and proposed production wells. The simulations were run to steady-state conditions, providing an estimate of the long-term effects of changes in withdrawal and recharge on the groundwater resource. Results of the simulated future withdrawal scenarios indicate that, relative to 2017–18 rates, the scenarios’ long-term effect of increased withdrawals ultimately leads to lower water levels and a higher 50-percent ocean-water salinity surface indicating a thinning of the freshwater lens. Results also indicate that the increased withdrawals produce some groundwater with chloride concentration below 250 milligrams per liter and some groundwater with higher chloride concentration. The amount of drawdown near production wells and the quality of water withdrawn from production wells is dependent on the rate and spatial distribution of the withdrawals.
The model was also used to evaluate how groundwater availability may be affected for a drier recharge scenario based on a published study of future climate. Model results of the future recharge scenario indicate that the rate of groundwater recharge is a controlling factor for (1) water levels, (2) the 50-percent ocean-water salinity surface, and (3) the quality of water withdrawn from production wells in the Wailuku area. Coupled with reduced groundwater recharge (with all other factors remaining equal), the modeled future withdrawals in the scenario would tend to cause lower water levels, a higher 50-percent ocean-water salinity surface, and increased salinity of water withdrawn from production wells.
The three-dimensional numerical groundwater model developed for this study utilizes the latest available hydrologic and geologic information and is a useful tool for understanding the long-term hydrologic effects of additional groundwater withdrawals in central Maui. The model has several limitations, including its non-uniqueness and inability to account for local-scale heterogeneities. Short-term effects of changes in recharge and withdrawals—and optimization of pumping rates to meet increased demand for water with acceptable salinity—are possible conditions for future simulation analyses.
Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Ecosystem metabolism quantifies the rate of production, maintenance, and decay of organic matter in terrestrial and aquatic systems. It is a fundamental measure of energy flow associated with biomass production by photosynthesizing organisms and biomass oxidation by respiring plants, animals, algae, and bacteria (Bernhardt et al., 2022) . Ecosystem metabolism also provides an understanding of energy flow to higher trophic levels that supports secondary and tertiary productivity, as well as helping to explain when aquatic ecosystems undergo out-of-balance behaviors such as harmful algal blooms and hypoxia. Recent advances in sensor technology and modeling capabilities have enabled estimation of aquatic system metabolism and gas exchange over long time periods in rivers, streams, ponds, and wetlands where oxygen sensors have been deployed. Here we present RiverMET, a framework for estimation of river metabolism, with workflows to streamline data preparation, run a stream metabolism model, assess the model performance, and flag and censor final output data. The workflows are specifically tailored to use streamMetabolizer, a model for one-station calculations of stream metabolism that calculates gross primary productivity (GPP), ecosystem respiration (ER) and the air-water gas exchange rate constant (K600). We advise potential users of RiverMET to review core publications for the streamMetabolizer model (Appling et al., 2018 a, b, c) to ensure best practices that produce the most useful results. We encourage feedback about our workflows, although issues regarding the streamMetabolizer model itself should be referred to the model authors. We tested RiverMET by calculating GPP, ER, and K600 across 17 river sites in the Illinois River basin (ILRB). Each river had between one and nine years of sensor data appropriate for modeling metabolism. In total, metabolism was modeled on 15,176 days between 2005 and 2020. Overall confidence in the results was rated as high at nine river sites, medium at six river sites, and poor at two river sites. Twenty-nine percent of the total modeled days had performance metrics that triggered flags. Metrics used for daily flagging are provided with the final output, with an option to only retain the censored daily outputs with high confidence (representing 72 %, i.e., 10,938 days, of the total days modeled). This work was completed as part of the U.S. Geological Survey Proxies Project, an effort supported by the Water Mission Area (WMA) Water Quality Processes program to develop estimation methods for harmful algal blooms (HABs), per- and polyfluoroalkyl substances (PFAS), and metals, at multiple spatial and temporal scales.
","language":"English","publisher":"Earth and Space Science Open Archive","doi":"10.1002/essoar.10511255.1","usgsCitation":"Choi, J., Quion, K.M., Reed, A., and Harvey, J., 2022, River Metabolism Estimation Tools (RiverMET) with demo in the Illinois River Basin: ESSOAr, 22 p., https://doi.org/10.1002/essoar.10511255.1.","productDescription":"22 p.","ipdsId":"IP-139945","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":435833,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TEBOUR","text":"USGS data release","linkHelpText":"RiverMET: Workflow and scripts for river metabolism estimation including Illinois River Basin application, 2005 - 2020"},{"id":409056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Illinois River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.901423683579,\n 42.70071815175049\n ],\n [\n -91.86724399607925,\n 42.70071815175049\n ],\n [\n -91.86724399607925,\n 39.14935275277796\n ],\n [\n -86.901423683579,\n 39.14935275277796\n ],\n [\n -86.901423683579,\n 42.70071815175049\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Choi, Jay 0000-0003-1276-481X jchoi@usgs.gov","orcid":"https://orcid.org/0000-0003-1276-481X","contributorId":219096,"corporation":false,"usgs":true,"family":"Choi","given":"Jay","email":"jchoi@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":856403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quion, Katherine Michelle Bernabe 0000-0003-2388-7508","orcid":"https://orcid.org/0000-0003-2388-7508","contributorId":298787,"corporation":false,"usgs":true,"family":"Quion","given":"Katherine","email":"","middleInitial":"Michelle Bernabe","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856404,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Ariel 0000-0002-0792-5204","orcid":"https://orcid.org/0000-0002-0792-5204","contributorId":298788,"corporation":false,"usgs":false,"family":"Reed","given":"Ariel","affiliations":[],"preferred":false,"id":856405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856406,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232211,"text":"70232211 - 2022 - Golden Eagle (Aquila chysaetos)","interactions":[],"lastModifiedDate":"2022-06-28T16:02:50.008235","indexId":"70232211","displayToPublicDate":"2022-04-22T10:56:50","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"8","displayTitle":"Golden Eagle (Aquila chysaetos)","title":"Golden Eagle (Aquila chysaetos)","docAbstract":"The golden eagle (Aquila chrysaetos) is commonly recognized as an indicator of ecosystem health and was selected as an important indicator species for the ecological health of lands owned and managed by East Bay Stewardship Network (Network) partner agencies within the area of focus for this project (See map, Chapter 1). Based on national conservation goals and past and current golden eagle research in the area of focus, the desired condition and trend for this indicator species are to: (1) maintain or improve site occupancy by territorial pairs (i.e., the proportion of sites surveyed with at least 1 pair of eagles), (2) maximize reproductive rate (i.e., the proportion of sites surveyed with at least 1 pair of productive eagles), and (3) minimize the occurrence of territorial subadults in the local breeding population. The condition and trend in these three primary metrics were assessed for golden eagles in the area of focus using data from a large-scale demographic study conducted in 2014–2021 by the U.S. Geological Survey (USGS) and others. Overall, we found a condition of “caution” and an “unchanging” trend for golden eagles in the area of focus. Analyses of site occupancy and reproductive rate indicated that the local breeding population was unchanging (i.e., no evidence of increasing or decreasing time trends in these metrics during 2014–2021). However, a consistently high occurrence of territorial subadults (22%–35%) has been observed at breeding territories near the Altamont Pass Wind Resource Area (APWRA) relative to occupied territories monitored in surrounding regions (~3%). The heightened occurrence of territorial subadults suggested a possible increase in the adult mortality rate of territorial eagles occupying the Mt. Diablo Range and Mt. Hamilton subregions in the area of focus. Thus, although no trends were detected in site occupancy or reproductive rate, caution is warranted given the high observed frequency of territorial subadults, which was predominately associated with pairs monitored near the APWRA. The USGS golden eagle study was conducted during a period of prolonged and severe drought in the area of focus, which has been shown elsewhere to reduce the reproductive rate of golden eagles. Although we detected no trends in reproductive rate, we identified a condition of “caution” for this metric in the area of focus given that annual estimates were relatively low during the study period, which primarily included years of severe drought conditions in west-central California. A primary goal of the analysis was to provide a benchmark against which managers can measure future changes and understand the likely trajectory of this species. Baseline data and analyses provided here can be used to identify projects that could help support golden eagle conservation. Given the constraint of using only existing and available data, this evaluation also identified areas where not enough was known to draw meaningful conclusions. Gaps in our understanding include the long-term effects of repeated, extreme climate events (e.g., drought and wildfire) on golden eagle demographics and population sustainability, refined estimates of eagle survivorship and sources of mortality, and whether the APWRA represents a population sink for golden eagles within the northern Diablo Range and surrounding regions. These are described as data gaps at the end of this chapter and may be areas to focus on for future research and collaborations among land managers.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"NatureCheck: Understanding wildlife health on East Bay lands in Alameda and Contra Costa Counties","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"East Bay Stewardship Network","usgsCitation":"Wiens, D., Kolar, P., and Bell, D.A., 2022, Golden Eagle (Aquila chysaetos), chap. 8 of NatureCheck: Understanding wildlife health on East Bay lands in Alameda and Contra Costa Counties, p. 211-244.","productDescription":"34 p.","startPage":"211","endPage":"244","ipdsId":"IP-137929","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":402603,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":402602,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.ebparks.org/natural-resources/biodiversity/wildlife"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wiens, David 0000-0002-2020-038X","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":267230,"corporation":false,"usgs":true,"family":"Wiens","given":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":844658,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolar, Patrick 0000-0002-0076-7565 pkolar@usgs.gov","orcid":"https://orcid.org/0000-0002-0076-7565","contributorId":189512,"corporation":false,"usgs":true,"family":"Kolar","given":"Patrick","email":"pkolar@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, Douglas A.","contributorId":292466,"corporation":false,"usgs":false,"family":"Bell","given":"Douglas","email":"","middleInitial":"A.","affiliations":[{"id":24634,"text":"East Bay Regional Park District","active":true,"usgs":false}],"preferred":false,"id":844660,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230152,"text":"sir20215103 - 2022 - Groundwater resources of the Harney Basin, southeastern Oregon","interactions":[],"lastModifiedDate":"2026-04-02T19:43:36.180655","indexId":"sir20215103","displayToPublicDate":"2022-04-11T15:18:39","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5103","displayTitle":"Groundwater Resources of the Harney Basin, Southeastern Oregon","title":"Groundwater resources of the Harney Basin, southeastern Oregon","docAbstract":"Groundwater development has increased substantially in southeastern Oregon’s Harney Basin since 2010, mainly for the purpose of large-scale irrigation. Concurrently, some areas of the basin experienced groundwater-level declines of more than 100 feet, and some shallow wells have gone dry. The Oregon Water Resources Department has limited new groundwater development in the basin until an improved understanding of the groundwater-flow system is available. This report describes the results of a hydrologic investigation undertaken to provide that understanding. The investigation encompasses the groundwater hydrology of the entire 5,240-square-mile Harney Basin.
Most of the precipitation in the Harney Basin falls in the higher-elevation areas of the Blue Mountains and Steens Mountain. Although considerable groundwater recharge occurs in these upland areas, most (83 percent) re-emerges as streams and springs in the uplands. Groundwater recharge in the lowlands is provided through infiltration of surface water flowing onto the lowlands from rivers and streams leaving the uplands and as groundwater flow from the surrounding upland rocks. Water-balance calculations indicate that the rate of groundwater recharge to the Harney Basin lowlands (where most groundwater is withdrawn) averages 173,000 acre-feet per year (acre-ft/yr).
Groundwater in the Harney Basin lowlands mainly discharges through evapotranspiration from groundwater-irrigated (supplied from wells) crops or from natural vegetation drawing groundwater from the shallow water table and capillary fringe. Groundwater discharge in the lowlands is estimated to be about 283,000 acre-ft/yr, which exceeds the estimated groundwater recharge to the lowlands by about 110,000 acre-ft/yr. This imbalance results in removal of groundwater from storage in the aquifer system and is evidenced by the large declines observed in groundwater levels in the areas of greatest groundwater pumpage.
To a large degree, the location and depth of pumpage dictate the timing and distribution of the effects of groundwater use in the Harney Basin. Pumpage is commonly greatest in the areas where higher-permeability geologic units allow for higher well yields. However, many of these higher-permeability units are bounded by lower-permeability units that cannot supply groundwater at a sufficient rate to replenish the areas of greatest pumpage, resulting in groundwater-level declines. Three Harney Basin areas with a combined area exceeding 140 square miles have experienced groundwater-level declines exceeding 40 feet compared to pre-development conditions: near the Weaver Spring/Dog Mountain area, in the northeastern floodplains along Highway 20, and near Crane. Areas of more modest groundwater-level decline (about 10 feet) were identified in the Virginia Valley area and the Silver Creek floodplain north of Riley. Smaller localized areas of groundwater-level depression have also formed around individual wells or groups of wells throughout the Harney Basin lowlands.
Most groundwater being pumped from the Harney Basin lowlands, including all three areas experiencing large groundwater-level declines, was recharged more than 12,000 years ago, near the end of the last glacial period when the climate in the basin was cooler and wetter than today. Geochemical evidence indicates that modern recharge generally circulates to a depth no greater than 100 feet below the floodplains of major rivers and streams in the lowlands. Away from the major river and stream corridors, pre-modern water commonly is found at the water table. Recharge to groundwater and recovery of groundwater levels in the most heavily pumped areas in the Harney Basin lowlands are restricted by the limited spatial extent and depth of modern recharge in the Harney Basin lowlands and the relatively fine-grained deposits underlying most of the lowland areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215103","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Gingerich, S.B., Johnson, H.M., Boschmann, D.E., Grondin, G.H., and Garcia, C.A., 2022, Groundwater resources of the Harney Basin, southeastern Oregon: U.S. Geological Survey Scientific Investigations Report 2021–5103, 118 p., https://doi.org/10.3133/sir20215103.","productDescription":"Report: xii, 118 p.; 3 Plates: 30.00 x 42.00 inches or smaller; 2 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119872","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":502118,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112850.htm","linkFileType":{"id":5,"text":"html"}},{"id":397922,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5103/"},{"id":397921,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5103/images"},{"id":397920,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103_plate03.pdf","text":"Plate 3","size":"10.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103 Plate 3"},{"id":398172,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J0FE5M","text":"USGS data release","description":"USGS Data Release.","linkHelpText":"Location information, discharge, and water-quality data for selected wells, springs, and streams in the Harney Basin, Oregon"},{"id":398171,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZJTZUV","text":"USGS data release","description":"USGS Data Release.","linkHelpText":"Contour data set of the potentiometric surfaces of shallow and deep groundwater-level altitudes in Harney Basin, Oregon, February–March 2018"},{"id":397917,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103.pdf","text":"Report","size":"28.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103"},{"id":397918,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103_plate01.pdf","text":"Plate 1","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103 Plate 1"},{"id":397916,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5103/coverthb.jpg"},{"id":397919,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103_plate02.pdf","text":"Plate 2","size":"27.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103 Plate 2"}],"country":"United States","state":"Oregon","otherGeospatial":"Harney Basin","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}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Organic matter (OM) accumulation in organic matter-rich mudstones, or black shales, is generally recognized to be controlled by combinations of bioproductivity, preservation, and dilution. However, specific triggers of OM deposition in these formations are commonly difficult to identify with geochemical proxies, in part because of feedbacks that cause geochemical proxies for these controls to vary synchronously. This apparent synchronicity is partly a function of sample spacing, commonly at decimeter to meter intervals, which may represent longer periods of time than is required for the development of feedbacks. Higher resolution data sets may be required to fully interpret OM accumulation.
This study applies a novel combination of technologies to develop a high-resolution geochemical data set, integrating energy-dispersive X-ray fluorescence (EDXRF) and infrared imagery analyses, to record proxies for redox conditions, bioproductivity, and clastic and carbonate dilution in millimeter-resolution profiles of 133 core slabs from the Middle and Upper Devonian Horn River shale in the Western Canada Sedimentary Basin, which provides decadal-scale temporal resolution. A comparison to a more coarsely sampled data set from the same core results in substantially different interpretations of variations in bioproductivity, redox, and dilution proxies. Stratigraphic distributions of organic matter accumulation patterns (bioproductivity-control, siliciclastic/carbonate-dilution, and redox conditions-control) show that organic enrichment events were highly varied during deposition of the shale and were closely related to second- and third-order sea-level changes. High-resolution profiles indicate that bioproductivity was the predominant trigger for organic matter accumulation in a second-order highstand, particularly during deposition of third-order transgressive systems tracts. Organic matter accumulation was largely controlled by dilution from either carbonate or clastic sediments in a second-order lowstand. Bioproductivity-redox feedbacks developed on timescales of decades to centuries.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B36091.1","usgsCitation":"Zhou, H., Harris, N.B., Dong, T., Ayranci, K., Feng, J., Rivard, B., Hackley, P.C., and Hatcherian, J.J., 2022, New insights into organic matter accumulation from high-resolution geochemical analysis of a black shale: Middle and Upper Devonian Horn River Group, Canada: Geological Society of America Bulletin, v. 134, no. 7-8, p. 2130-2144, https://doi.org/10.1130/B36091.1.","productDescription":"15 p.","startPage":"2130","endPage":"2144","ipdsId":"IP-126339","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":449362,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/gsab.s.17054183","text":"External Repository"},{"id":398462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"British Columbia, Northwest Territories","otherGeospatial":"Horn River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.91430664062499,\n 57.48040333923341\n ],\n [\n -120.531005859375,\n 57.48040333923341\n ],\n [\n -120.531005859375,\n 61.079544234557304\n ],\n [\n -125.91430664062499,\n 61.079544234557304\n ],\n [\n -125.91430664062499,\n 57.48040333923341\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"134","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2021-12-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhou, Haolin","contributorId":289963,"corporation":false,"usgs":false,"family":"Zhou","given":"Haolin","email":"","affiliations":[],"preferred":false,"id":840106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Nicholas B.","contributorId":289966,"corporation":false,"usgs":false,"family":"Harris","given":"Nicholas","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":840107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dong, Tian","contributorId":239901,"corporation":false,"usgs":false,"family":"Dong","given":"Tian","email":"","affiliations":[{"id":48038,"text":"Institute for Geophysics and Department of Geological Sciences, Jackson School of Geosciences, University of Texas","active":true,"usgs":false}],"preferred":false,"id":840108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ayranci, Korhan","contributorId":289969,"corporation":false,"usgs":false,"family":"Ayranci","given":"Korhan","email":"","affiliations":[],"preferred":false,"id":840109,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feng, Jilu","contributorId":289972,"corporation":false,"usgs":false,"family":"Feng","given":"Jilu","email":"","affiliations":[],"preferred":false,"id":840110,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rivard, Benoit","contributorId":289973,"corporation":false,"usgs":false,"family":"Rivard","given":"Benoit","email":"","affiliations":[],"preferred":false,"id":840111,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":840112,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":840113,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70226624,"text":"sim3480 - 2021 - Geologic map of the Aeolis Dorsa Region, Mars","interactions":[],"lastModifiedDate":"2023-03-20T18:15:52.30554","indexId":"sim3480","displayToPublicDate":"2021-12-14T09:40:05","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3480","displayTitle":"Geologic Map of the Aeolis Dorsa Region, Mars","title":"Geologic map of the Aeolis Dorsa Region, Mars","docAbstract":"The Aeolis Dorsa region of Mars, located just north of the global dichotomy boundary, includes the Aeolis and Zephyria Plana, and a depositional basin between them. This interplana region consists of extensive networks of ridges—the eponymous Aeolis Dorsa—and is interpreted as having formed by topographic inversion of fluvial and alluvial deposits. To the south is a nearly 1-km-deep trough (Aeolis Chaos) and the southern highlands. These elements of the map area compose a landscape of extensive erosional and depositional sedimentary processes. The plana are pervasively abraded into yardangs, and the interplana area shows scattered yardangs superposed on the underlying terrain. The Aeolis Dorsa fluvial deposits are concentrated within and around the margins of the interplana region, exposed and (or) inverted by this pervasive aeolian abrasion. During this period of extensive erosion and deposition, impacts have also redistributed material. The geologic mapping of this region, conducted at 1:500,000 scale to enable depiction of the fine-scale fluvial features, divides the landscape into six unit groups. The highlands units group comprises three units, located in the southwestern map area. These units, having the highest elevation in the map area, consist of mesas surrounded by more gently sloping terrain. The transitional units group, located northeast of the highlands units, includes the Aeolis Chaos, denoted as a chaos terrain unit, and two transitional units, differentiated on the basis of surface texture and relative elevation. The plana units group comprises five units. The oldest unit consists of mesas similar to those of the highlands mesas unit but located about 200 kilometers north of the highlands in Aeolis Planum. Three other plana units, located on the Aeolis and Zephyria Plana, are differentiated on the basis of yardang texture, crosscutting relations, and relative elevations. They are interpreted as abraded sedimentary and (or) volcaniclastic deposits. The fifth plana unit, which crops out in the north corners of the map area, is at low elevation, has numerous small craters, and is interpreted as cratered lava plains. The interplana units group hosts a hummocky unit and a mounds unit, differentiated on the basis of texture and relief. In the Aeolis Dorsa units group, four units are mapped on the basis of dorsa morphology and adjacent textures. Stratigraphic relations indicate a decrease in discharge over time. The crater units group includes a crater unit, found throughout the map area although concentrated within the interplana region, and a crater fill unit that is found within several craters in this interplana region. Based on this mapping, the interpretation of the regional geologic record begins with emplacement of the highlands terrain during the Noachian Period. Emplacement was followed during the Early Hesperian by erosion and redistribution of this high-standing terrain to form the transitional units, and Aeolis Chaos formed after deposition of the other transitional units. The high-standing plana were emplaced and eroded repeatedly throughout the Hesperian and Early Amazonian time, and impact cratering occurred at decreasing crater sizes. The fluvial and alluvial activity that gave rise to the Aeolis Dorsa also extended throughout this time, leaving their diagnostic signature on this region.
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Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Hydrologic records used to create previously published maps depicting mean annual runoff are biased to a relatively dry period in Oklahoma history that was dominated by droughts. Therefore, the U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, developed an updated mean annual runoff and annual runoff variability map for Oklahoma and parts of adjacent States. The updated map, which is based on mean-annual-streamflow regression equations developed from available streamgage data through 2007, is assumed to be representative of the long-term mean annual runoff conditions. The map covers all 69 8-digit hydrologic units with at least 1 square mile of area in Oklahoma; those 8-digit hydrologic units contain 2,870 12-digit hydrologic units that provided the geographic framework for the analysis described in this report. Although parts of adjacent States are included in the study area, this report is primarily focused on providing a map of mean annual runoff and annual runoff variability for Oklahoma.
The mean annual runoff increased from less than 0.25 inch per year in the Panhandle of northwestern Oklahoma to more than 30 inches per year in the mountainous terrain of southeastern Oklahoma. The orientation and pattern of mean annual runoff contours in this report were comparable to those of previously published map reports. The annual runoff variability, or the difference between the 80-percent and 20-percent streamflow-duration statistics, increased from less than 0.25 inch per year in the Panhandle of northwestern Oklahoma to more than 40 inches per year in the mountainous terrain of southeastern Oklahoma. The annual runoff variability data were similar in orientation and pattern to the mean annual runoff contours; annual runoff variability generally increased proportionally with increasing mean annual runoff. The annual runoff variability was also greatest, therefore, in the mountainous terrain of southeastern Oklahoma.
The mean annual runoff and annual runoff variability were calculated at sampled points representing the outlets of 12-digit hydrologic units, so the map in this report is most representative of runoff conditions in rural, unregulated drainage basins at the 12-digit hydrologic-unit scale. The map was developed by using regression equations formulated on streamgage data for the entire period of record through 2007, but those equations are biased to the period 1940–2007 when streamgages became more numerous and distributed across Oklahoma. Therefore, the map is likely most representative of runoff conditions during the period 1940–2007. Because runoff is a function of climate variables that can change over time, caution is warranted when using the information in this report to project mean annual runoff and annual runoff variability conditions beyond 2007.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3482","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Smith, S.J., and Sherrod, E.M., 2021, Mean annual runoff and annual runoff variability map for Oklahoma, 1940–2007: U.S. Geological Survey Scientific Investigations Map 3482, 1 sheet, scale 1:100,000, 10-p. pamphlet, https://doi.org/10.3133/sim3482.","productDescription":"Pamphlet: vi, 10 p.; Sheet: 34.00 x 24.00 inches; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-127939","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":392378,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SG5ZDO","text":"USGS data release","linkHelpText":"Data release for mean annual runoff and annual runoff variability map for Oklahoma, 1940–2007"},{"id":392375,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3482/coverthb.jpg"},{"id":392376,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3482/sim3482_sheet.pdf","text":"Map","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3482 Sheet"},{"id":392377,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3482/sim3482_pamphlet.pdf","text":"Pamphlet","size":"994 kB","description":"SIM 3482 Pamphlet"}],"country":"United 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U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501