The Mississippi Alluvial Plain is one of the most important agricultural regions in the United States, and crop productivity relies on groundwater irrigation from an aquifer system whose full capacity is unknown. Groundwater withdrawals from the Mississippi River Valley alluvial aquifer have resulted in substantial groundwater-level declines and reductions in base flow in streams within the Mississippi Alluvial Plain. These effects are limiting well production and threatening future water availability in the region.
A comprehensive assessment of water availability in the Mississippi Alluvial Plain is critically important for making well-informed management decisions about sustainability, establishing best practices for water use, and predicting changes to water levels in the Mississippi Alluvial Plain over the next 50–100 years. The first step in the new regional modeling effort was to run the existing Mississippi Embayment Regional Aquifer Study (MERAS) model and perform data-worth and uncertainty analyses to prioritize data collection efforts to improve model forecasts. Parameter estimation indicated that streambed conductance was one of the variables that the model was most sensitive to, but little data were available to constrain those general estimates.
From this characterization of the existing data, a map of the streams that the MERAS model was most sensitive to was created by the U.S. Geological Survey to guide the collection of 862 kilometers of waterborne resistivity surveys within the Delta region of Mississippi to characterize streambed lithology. This technique characterizes the streambed itself and the 15–30 meters below the streambed that control the exchange of water between the stream and the alluvial aquifer. These data can be used to map changes in the lithology of the streambed and identify areas of potential groundwater/surface-water exchange. Additionally, electrical and nuclear well logs from the study area were compared to facilitate the development of a petrophysical relation between the waterborne resistivity data and hydraulic conductivity. Resistivity values may then be used as a cost-effective way to approximate aquifer hydraulic conductivity distributions for use in regional groundwater models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3500","issn":"2329-132X","collaboration":"Prepared in cooperation with the Arkansas Department of Health, Arkansas Game and Fish Commission, Delta Council, Delta FARM, Delta Sustainable Water Resources Task Force, Delta Wildlife HydroGeophysics Group, Aarhus University, Mississippi Department of Environmental Quality, Mississippi State University, Missouri Department of Natural Resources, The Nature Conservancy, U.S. Army Corps of Engineers, U.S. Department of Agriculture-Agricultural Research Service, University of Arkansas, University of Mississippi, Yazoo Mississippi Delta Joint Water Management District","usgsCitation":"Adams, R.F., Miller, B.V., Kress, W.H., Minsley, B.J., and Rigby, J.R., 2023, Estimating streambed hydraulic conductivity for selected streams in the Mississippi Alluvial Plain using continuous resistivity profiling methods—Delta region: U.S. Geological Survey Scientific Investigations Map 3500, 2 sheets, https://doi.org/10.3133/sim3500.","productDescription":"2 Sheets: 45.00 x 34.25 inches and 45.00 x 34.55 inches","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-115128","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":419523,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WQPRFB","text":"USGS—Waterborne resistivity inverted models, Mississippi Alluvial Plain, 2016–2018"},{"id":419522,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3500/sim3500_sheet2.pdf","size":"14.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3500 sheet 2"},{"id":419521,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3500/sim3500_sheet1.pdf","size":"15.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3500 sheet 1"},{"id":419520,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3500/coverthb.jpg"},{"id":500206,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115125.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arkansas, Louisiana, Mississippi","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90,\n 35\n ],\n [\n -91.25,\n 35\n ],\n [\n -91.25,\n 31\n ],\n [\n -90,\n 31\n ],\n [\n -90,\n 35\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
For additional information, visit
https://www.usgs.gov/centers/lmg-water/
Annual peak-flow data collected at U.S. Geological Survey streamgages in Minnesota and adjacent areas of neighboring states of Iowa and South Dakota were analyzed to develop and update regional regression equations that can be used to estimate the magnitude and frequency of peak streamflow for ungaged streams in Minnesota, excluding the Lake of the Woods-Rainy River Basin upstream from Kenora, Ontario, Canada. Hydraulic engineers use peak-flow frequency estimates to inform designs of bridges, culverts, and dams, and water managers use the estimates for regulation and planning activities. Peak-flow estimates are provided for the 66.7-, 50-, 20-, 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities (AEPs), which are equivalent to annual flood-frequency recurrence intervals of 1.5-, 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively. The estimates were computed by applying the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows for 298 streamgages based on annual peak-flow data collected through water year 2019. The study area is represented by six hydrologic regions delineated on the basis of a pattern of residuals of statewide regressions, using basin characteristics such as drainage area, main-channel slope, lake area, storage area, and mean annual runoff as explanatory variables. The concept and principles of hydrologic landscape units was used to validate the regions. Residual analysis of the regional regression equations was used to subsequently develop equations relating the peak flow estimates for selected AEPs using 17 characteristics tested as explanatory variables in the regression analysis.
The equations developed in this study can be used to produce AEPs within the six regions and to update equations developed in earlier, similar studies in Minnesota. Furthermore, updating the equations in StreamStats, a web-based geographic information system tool developed by the U.S. Geological Survey, will allow hydraulic engineers and water managers to obtain AEPs and basin characteristics for user-selected locations on streams through an interactive map.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235079","collaboration":"Prepared in cooperation with the Minnesota Department of Transportation","usgsCitation":"Sanocki, C.A., and Levin, S.B., 2023, Techniques for estimating the magnitude and frequency of peak flows on small streams in Minnesota, excluding the Rainy River Basin, based on data through water year 2019: U.S. Geological Survey Scientific Investigations Report 2023–5079, 15 p., https://doi.org/10.3133/sir20235079.","productDescription":"Report: v, 15 p.; 2 Data Releases; Dataset","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-127955","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":419307,"rank":7,"type":{"id":39,"text":"HTML 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\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Quaternary marine and continental shales in the western United States are sources of selenium that can be loaded into the aquatic environment through mining, agricultural, and energy production processes. The mobilization of selenium from shales through agricultural irrigation has been recognized since the 1930s; however, discovery of deformities in birds and other wildlife using agricultural habitats during the 1980s spurred studies to determine the extent and effects of the contamination. Through these early studies, researchers determined that biota in the Salton Sea drainage basin was at risk from legacy selenium contamination in the Colorado River watershed.
The Salton Sea and its surrounding managed and unmanaged wetlands provide vital inland habitat and trophic support for diverse assemblages of resident and migratory wildlife, and understanding regional selenium hazards for these trust species is a priority for many Federal and State agencies. The modern Salton Sea is a shallow, landlocked saline lake in Riverside and Imperial Counties (not shown) of California that is sustained by irrigation return and perennial river inflow. Changes in water transfer agreements under the 2003 Quantification Settlement Agreement (QSA) have resulted in reduced irrigation flow, declining lake levels, and the evolution of unmanaged wetlands in areas where drains and rivers no longer reach the Salton Sea. These wetlands provide additional habitat for some species of concern, but their potential to increase selenium hazards for trust species is largely unknown.
From the 1980s to 2020, efforts to document selenium contamination and effects throughout the region have resulted in a considerable amount of selenium data from the Salton Sea and its surrounding drainage basin; however, no long-term (greater than 20 years), consistent sampling program has been established, and all data have been collected by different entities using a variety of protocols and analytical techniques. This lack of coordination has been previously documented in regional management plans and has led to difficulty in reliably assessing selenium hazards in the Salton Sea environment. This report provides a summary of the available disparate selenium information collected from water, sediment, and biota in the Salton Sea region since the 1980s and to identify data gaps that need to be filled to understand the potential effects of selenium on species of concern, including federally endangered desert pupfish (Cyprinodon macularius) and Yuma Ridgway’s Rail (Rallus obsoletus yumanensis; formerly Yuma Clapper Rail, Rallus longirostris yumanensis).
Available data from the Salton Sea drainage basin show that water from the Colorado River has the lowest selenium concentration of all surface water sources. All other surface water flowing into the Salton Sea has elevated selenium concentrations due to evaporation and evapotranspiration that occurs in agricultural fields and associated water delivery infrastructure or leaching of selenium from irrigated farmland soils. The Salton Sea has lower selenium concentrations because of various biogeochemical processes that recycle selenium into the sediment or volatilize it to the atmosphere; however, these mechanisms are not well defined, and it is not clear if selenium cycling will change in response to possible changes in the oxidation state of the Salton Sea bottom waters as water levels decline. Agricultural drains have the highest average selenium concentrations, but few drains have been sampled since changes in irrigation practices have occurred (due to the 2003 QSA). Groundwater selenium concentrations are variable; some wells south of the Salton Sea have selenium concentrations as high as 300 micrograms per liter (µg/L), whereas selenium concentrations are below detection in other wells. Groundwater and surface-water geothermal discharge zones around the margins of the Salton Sea and in unmanaged wetlands have not been studied in detail, and published selenium measurements are not available for these surface features.
Selenium concentrations in the sediment of the Salton Sea drainage basin are highest in wetland particulate organic matter and the Salton Sea lakebed, indicating that removal of selenium from the water to the sediment has been a primary mechanism for keeping selenium concentrations low in the water column. Sediment selenium concentrations in wetlands are lower than in the Salton Sea but higher than inflowing drains and rivers, indicating the lentic wetland sites also may be important sinks for selenium because of biogeochemical processes. Sediment selenium data have not been collected in agricultural drains since changes in irrigation practices occurred (due to the 2003 QSA), and it is unknown if selenium sequestration from the water column has changed in these systems.
We divided biological data into broad taxonomic categories, including primary producers, invertebrates, herpetofauna, mammals, fishes, and birds to facilitate evaluation of selenium concentrations and spatiotemporal trends observed in the Salton Sea. Overall, selenium concentrations were substantially greater in algae samples compared to all vascular plant samples combined. Median selenium concentrations in several invertebrate taxa (Chironomidae, Formicidae, Corixidae, Corbiculidae and Nereididae, and Decapoda) exceeded the maximum suggested dietary threshold of 3.0–4.0 micrograms per gram (µg/g) dry weight (dw) for predators consuming invertebrates in aquatic food webs. The greatest number of samples were collected from fish, and selenium distributions among species and locations showed that the range for most samples was lower than the U.S. Environmental Protection Agency selenium criterion for aquatic life (8.5 µg/g dw whole body, 11.3 µg/g dw fillets). The median selenium concentrations for whole body fish were below the selenium criterion in most locations, except for bairdiella (Bairdiella icistia) from the Salton Sea and irrigation drains, a few individual tilapia spp. (family Cichlidae, including genera Tilapia, Oreochromis, and their hybrids) from the river and river outlets, and several western mosquitofish (Gambusia affinis) and sailfin molly (Poecilia latipinna) from irrigation drain outlets. For avian samples combined among years and locations, median selenium concentrations in livers from all families except waders and Ibis (family Threskiornithidae) were higher than levels expected to cause selenium toxicosis (10–20 µg/g dw), and all median egg concentrations were above or near 6.0 μg/g dw, which is a conservative threshold value for reproductive impairment.
Most knowledge gaps we identified for water, sediment, and biota were interrelated, and the use of integrated approaches to address knowledge gaps can provide greater insight into the drivers behind selenium hazards. Integrated water, sediment, and biota studies could help identify cost-effective management solutions that serve multiple purposes. A comprehensive analysis of the hydrology, biogeochemistry, and food-web processes in wetlands and other habitats can inform predictive models to identify drivers of selenium bioavailability, uptake from the environment and subsequent trophic transfer, ultimately forming the basis for experimental habitat management manipulations to minimize selenium hazards to wildlife. Furthermore, a comprehensive, long-term sampling and analytical laboratory plan would enable comparison of data among different entities that are sampling at the Salton Sea. Such efforts are well suited to help fill knowledge gaps that preclude understanding of selenium hazards and future management options for biota using Salton Sea habitats, including newly formed wetlands throughout the region.
All data compiled for this report are available in two U.S. Geological Survey data releases: Groover and others (2022) for water and sediment samples and De La Cruz and others (2022) for biological samples. The data releases include all publicly available data for selenium concentrations in water, sediment, and biological samples collected in and around the Salton Sea, including the Coachella and Imperial Valleys. The data releases also include previously unpublished data.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
River turbulence is spatially variable due to interactions between morphology of rivers and physical mechanics of flowing water. Understanding the variation of turbulence in rivers is important for characterizing transport processes of soluble and particulate materials in these systems. We present an exploratory effort to understand ecologically relevant flow patterns using measurements of mean flow and turbulence in a highly engineered river channel around an island in the lower Missouri River. Specifically, the profiles of mean river velocities were investigated to examine the logarithmic relation and associated parameters, including shear velocity and bed roughness. Turbulence intensity and Reynolds shear stress were compared with classic open-channel profiles and previously reported river data in the hydraulics literature. With the capability of pulse-to-pulse coherent Doppler velocity profiling in high spatial resolution, we estimated the profiles of turbulence dissipation rate using resolved one-dimensional velocity spectra. These measurement data allow us to examine the validity of turbulence production-dissipation balance and the classic open-channel profiles of turbulence statistics, including turbulence intensity, Reynolds shear stress, dissipation rate, and eddy viscosity. The field data show a strong variation of turbulence profiles in close vicinity of the river island. In shallow water depths close to the island, turbulence is substantially enhanced in comparison with classic open-channel profiles. Such turbulence enhancement is likely attributed to non-uniformity of the flow structures.
Grant-Kohrs Ranch National Historic Site (GRKO) in southwestern Montana commemorates the frontier cattle era and its formative role in shaping the culture and history of the Western United States. The ranch was designated a national historic landmark in 1960 and a unit of the National Park Service (NPS) by Congress in 1972. The GRKO is unique because of its proximity to large-scale extraction, milling, and smelting of gold, silver, copper, and lead ore from the 1860s to the 1980s in the Butte mining district. During this time, mining and milling wastes were discarded in the upper Clark Fork Basin, resulting in the deposition of large amounts of waste materials (tailings) enriched with metallic contaminants (including cadmium, copper, iron, lead, manganese, zinc, and the metalloid trace element arsenic) in soils and in nearby streams and floodplains. Denuded vegetation and fish kills attributed to large concentrations of heavy metals caused the U.S. Environmental Protection Agency to designate a 120-mile section of the Clark Fork River (hereafter referred to as the “Clark Fork”), including GRKO, to be included on the National Priority List for Superfund cleanup in 1989. In 2018, with oversight from the Montana Department of Environmental Quality, the NPS began remediation of 2.6 miles of the Clark Fork as it flows through GRKO property.
In 2019, the U.S. Geological Survey (USGS), in collaboration with the NPS, conducted a study using time-series data from backscatter signals from fixed-point turbidity and acoustic sensors with the intent to provide a high-resolution monitoring tool to estimate metallic-contaminant concentrations (MCCs) and loads during NPS remediation of the Clark Fork. Two monitoring sites at USGS streamgages on the Clark Fork on either side of GRKO property were instrumented with turbidity and acoustic sensors and surrogate relations were developed among time-series data and MCCs. The application of high-resolution surrogate data was used to infer contaminant source and fate and evaluate MCC values relative to aquatic-life standards. Using high-resolution surrogate data, it was determined that during spring runoff and storm-related runoff events, MCCs peaked at their highest values at streamflows markedly lower and prior to peak streamflow. Because MCCs peaked prior to streamflow peaks, it could be inferred that the source of MCCs originated from channel bed sediments in close spatial proximity to the monitoring site or from nearby streambanks and floodplains. High-resolution surrogate data revealed that copper concentrations in the Clark Fork exceeded chronic aquatic-life standards 90 percent of the time when streamflow exceeded 200 cubic feet per second (ft3/s) and exceeded acute aquatic-life standards 85 percent of the time when streamflow exceeded 260 ft3/s. These data helped support NPS management goals for evaluating variation in water quality during remediation of GRKO property, evaluating MCC values relative to aquatic-life standards, and quantifying benefits from Superfund remediation activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235021","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Ellison, C.A., Sando, S.K., and Cleasby, T.E., 2023, Application of surrogate technology to predict real-time metallic-contaminant concentrations and loads in the Clark Fork near Grant-Kohrs Ranch National Historic Site, Montana, water years 2019–20: U.S. Geological Survey Scientific Investigations Report 2023–5021, 70 p., https://doi.org/10.3133/sir20235021.","productDescription":"Report: x, 70 p.; Data Release; Dataset","numberOfPages":"84","onlineOnly":"Y","ipdsId":"IP-133560","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":417541,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9330BXM","text":"USGS data release","linkHelpText":"Water quality and streamflow data for the Clark Fork near Grant-Kohrs Ranch National Historic Site in southwestern Montana, water years 2019–20"},{"id":500715,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114935.htm","linkFileType":{"id":5,"text":"html"}},{"id":417543,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5021/images"},{"id":417542,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":417540,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5021/sir20235021.XML","text":"Report","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023–5021 XML"},{"id":417539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5021/sir20235021.pdf","text":"Report","size":"8.46 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5021"},{"id":418358,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023–5021"},{"id":417538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5021/coverthb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Clark Fork, Grant-Kohrs Ranch National Historic Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.99863083744248,\n 47.014564748966194\n ],\n [\n -113.99863083744248,\n 45.54540728416404\n ],\n [\n -112.31070324373364,\n 45.54540728416404\n ],\n [\n -112.31070324373364,\n 47.014564748966194\n ],\n [\n -113.99863083744248,\n 47.014564748966194\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Water resources in the coastal region of North Carolina and South Carolina (Coastal Carolinas) are currently under stress from competing ecological and societal needs. Projected changes in climate and population are expected to place even more stress on water resources in the region. The Coastal Carolinas Focus Area Study was initiated by the U.S. Geological Survey Water Availability and Use Science Program’s National Water Census to investigate these stressors and their effects on water resources for the Coastal Carolinas. As part of that study, the Soil and Water Assessment Tool (SWAT) model was used to investigate future streamflow and irrigation demand under six scenarios for the Cape Fear and Pee Dee River Basins, which flow through the Coastal Carolinas and into the Atlantic Ocean.
For each river basin, historical (2000 through 2014) Soil and Water Assessment Tool models were minimally calibrated, and future (2055 through 2065) scenario models were developed based on three alternative global climate models, two alternative urban growth projections, and water-use projections that correspond to each global climate model and urban growth projection pair. The river basins were delineated into 2,928 and 5,678 subbasins for the Cape Fear and Pee Dee, respectively, each approximately 2.6 square miles (mi2) in size. The best available water-use and wastewater discharge data were used for historical model calibration. The models simulated monthly mean streamflow with median Nash-Sutcliffe efficiency values of 0.53 (n = 36) and 0.61 (n = 33) in the Cape Fear and Pee Dee River Basins, respectively. Average percent bias was −4.8 percent for the Cape Fear River Basin and −1.2 percent for the Pee Dee River Basin. Catchments for streamgages chosen for model calibration that were small (less than 100 mi2) to medium (100–1,000 mi2) in area tended to perform better than larger catchments (greater than 1,000 mi2).
Historical models were used to develop future model scenarios by replacing historical weather, land-use, and water-use input datasets with projected datasets. One small, gaged catchment was selected to illustrate how the models can be used to evaluate the relative differences in simulated streamflow resulting from alternative global climate models and urban growth projections. For the selected catchment, future climate projections had a much greater influence on simulated streamflow than urban growth projections. Simulated cumulative monthly mean streamflow results for this catchment differed by 26 percent under alternative global climate models and differed by 2.4 percent under alternative urban growth projections.
Irrigation demand was modeled for subbasins with cropland. Simulated differences in irrigation demand were more pronounced and widespread across the model domain under the alternative future climate scenarios compared to alternative urban growth scenarios.
The calibrated and future scenario models have the capability to run on a daily time step and simulate streamflow and irrigation demand for thousands of small subbasins in the Cape Fear and Pee Dee River Basins. The models and underlying datasets enable future analyses for large and small areas within the basins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235036","issn":"2328-0328","programNote":"Water Availability and Use Science Program","usgsCitation":"Gurley, L.N., García, A.M., Pfeifle, C.A., and Sanchez, G.M., 2023, Simulation of future streamflow and irrigation demand based on climate and urban growth projections in the Cape Fear and Pee Dee River Basins, North Carolina and South Carolina, 2055–65: U.S. Geological Survey Scientific Investigations Report 2023–5036, 23 p., https://doi.org/10.3133/sir20235036.","productDescription":"Report: viii, 23 p.; 2 Data Releases","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-118241","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":417754,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P951VE5P","text":"USGS Data Release—Soil and Water Assessment Tool (SWAT) models for the Pee Dee River Basin used to simulate future streamflow and irrigation demand based on climate and urban growth projections"},{"id":417749,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5036/sir20235036.pdf","size":"12.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5036"},{"id":500906,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114934.htm","linkFileType":{"id":5,"text":"html"}},{"id":417753,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98PVDBW","text":"USGS Data Release—Soil and Water Assessment Tool (SWAT) models for the Cape Fear River Basin used to simulate future streamflow and irrigation demand based on climate and urban growth projections"},{"id":417752,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5036/images/"},{"id":417751,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235036/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5036 HTML"},{"id":417750,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5036/sir20235036.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5036 XML"},{"id":417748,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5036/coverthb.jpg"}],"country":"United States","state":"North Carolina, South Carolina","otherGeospatial":"Cape Fear and Pee Dee River Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.57121892850378,\n 32.91032390352079\n ],\n [\n -79.1296411830063,\n 33.177656538712625\n ],\n [\n -78.78619182539771,\n 33.72348311575605\n ],\n [\n -77.91939106571822,\n 33.920494939888584\n ],\n [\n -77.52687751416516,\n 34.40766001221573\n ],\n [\n -76.77455987368852,\n 35.02604160967191\n ],\n [\n -78.54904822133423,\n 36.169665661169745\n ],\n [\n -79.23594693655193,\n 36.51875607367013\n ],\n [\n -80.06186086794473,\n 36.51218395574713\n ],\n [\n -81.09220894077154,\n 36.66976065554749\n ],\n [\n -81.87723604387764,\n 35.792485806453826\n ],\n [\n -80.6996953892185,\n 34.985853498019594\n ],\n [\n -80.79782377710687,\n 34.42115219807647\n ],\n [\n -80.72422748619073,\n 33.5123831107352\n ],\n [\n -79.57121892850378,\n 32.91032390352079\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"For more information about this publication, contact
Program Coordinator
U.S. Geological Survey
Water Availability and Use Science Program
National Water Quality Program
Email: wausp-info@usgs.gov
For additional information, visit
https://www.usgs.gov/programs/national-water-quality-program
Declining water levels and the potential impact on water resources on and around Cannon Air Force Base (AFB), New Mexico, has necessitated an up-to-date review of the potentiometric surface to evaluate the availability of water resources for future use. Analysis of groundwater-flow directions and hydraulic gradients can provide an understanding of depletion by heavy groundwater pumping and recharge through playa lakes, as well as the relationship between the groundwater levels and underlying geology. The objectives of this study, conducted by the U.S. Geological Survey in cooperation with the U.S. Air Force Civil Engineer Center, are to assist Cannon AFB in understanding and interpreting current and local hydrologic conditions and to evaluate groundwater-level change from 2015 to 2020 using new and historical data. A groundwater potentiometric surface contour map was constructed to better understand the Southern High Plains aquifer around Cannon AFB and to show the altitude of the water-table surface and groundwater-flow patterns. Four hydrographs were created from periodic measurements of groundwater levels in wells on and around Cannon AFB to provide information about historical groundwater-level changes and visualize trends from the water-level records. The long-term trend present in all four hydrographs is a steady decline in groundwater levels, with some areas declining faster than others. The groundwater-level change map presented in this study provides a visual representation of the change in groundwater level from the winter 2015 to winter 2020 measuring events. Results show that among corresponding wells measured in 2015 and 2020, 50.7 percent indicated a decline in water levels, 29.9 percent indicated neutral water levels, and 19.4 percent indicated a rise in water levels. The region to the north of the groundwater trough on Cannon AFB contained most of the groundwater-level rises, whereas the regions located near the trough and just west of Clovis, N. Mex., contained most of the declines. These results suggest that continued monitoring of declining groundwater levels in the area would provide valuable decision-support information for assessing the sustainability of this water resource.
Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Two stations on the Galena River in Illinois were monitored for nitrogen, phosphorus, and suspended sediment from 2019 to 2021 to determine physiochemical properties and constituent concentrations, flux, and yields. This information could aide in the management and understanding of the Galena River and the contributions from the intervening 58-square-mile study area watershed. Constituent concentrations were characteristic for contemporary midwestern agricultural watersheds and did not display any notable high or low values. Concentrations of nitrogen were generally higher at the upstream station, whereas concentrations of phosphorus and suspended sediment were generally higher at the downstream station. Decreases in nutrient concentrations were observed at both stations during the study period, but there was no appreciable pattern in suspended-sediment concentrations. Constituent fluxes, particularly nitrogen, were higher at the downstream station, whereas fluxes of phosphorus and suspended sediment were higher at the upstream station during several high-flow events, indicating substantial contribution of particulate material upstream from the study area and potential sequestration within the study area reach of the Galena River. For all constituents, yields were typically higher at the upstream station during periods of increased streamflow and lower at the upstream station during periods of reduced streamflow. These data indicate that the constituent contributions are greater from within the study area than from the watershed upstream from the study area during periods of normal to low streamflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235044","collaboration":"Prepared in cooperation with the City of Galena, Illinois","usgsCitation":"Terrio, P.J., and Garcia, L.A., 2023, Nutrient and suspended-sediment concentrations, flux, and yields in the Galena River, Illinois, 2019–21: U.S. Geological Survey Scientific Investigations Report 2023–5044, 26 p., https://doi.org/10.3133/sir20235044.","productDescription":"Report: v, 26 p.; Dataset","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-146048","costCenters":[],"links":[{"id":500921,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114778.htm","linkFileType":{"id":5,"text":"html"}},{"id":418087,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235044/full"},{"id":418069,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":418068,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5044/images/"},{"id":418067,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5044/sir20235044.XML"},{"id":418066,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5044/sir20235044.pdf","text":"Report","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":418065,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5044/coverthb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Galena River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.5,\n 42.5\n ],\n [\n -90.5,\n 42.3\n ],\n [\n -90.20,\n 42.3\n ],\n [\n -90.20,\n 42.5\n ],\n [\n -90.5,\n 42.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The Grand Canyon in northern Arizona is an international tourist destination, a home or sacred place to many Native Americans, and hosts some of the highest-grade uranium deposits in the United States. Although potential contamination of water resources by uranium from mining activities is a concern, other elements commonly associated with these uranium deposits may pose a greater risk to human populations in the area. This study presents an assessment of arsenic in groundwater in the Grand Canyon area. First, sampling results for arsenic are presented and areas with elevated arsenic concentrations are discussed. Potential pathways of groundwater contamination by arsenic from uranium mines are then discussed to elucidate situations and conditions under which elevated concentrations of arsenic might be expected to become mobilized from breccia-pipe uranium mining activities. Results for arsenic in groundwater in the study area were available for 652 samples collected from 230 sites. Arsenic concentrations in groundwater ranged from less than reporting limits in 60 samples to a maximum concentration of 875 μg/L at Pumpkin Spring. About 88% (202) of the sites sampled had a maximum arsenic concentration below the drinking water standard of 10 μg/L. Available data from near former or current breccia-pipe uranium mines in the area indicate limited evidence to-date of mining effects on elevated arsenic in groundwater, although slow groundwater flow paths in the region may result in extended times of decades or more for groundwater to reach discharge locations. Post-mining entry of groundwater into the shaft and underground mine workings, with subsequent transport of metal-enriched groundwater offsite, may be a potential pathway of groundwater arsenic contamination from mining, although concentrations would likely be attenuated by contact with sedimentary rock units and dilution with native groundwater along flow paths. Monitoring of perched groundwater at reclaimed mine sites post-reclamation could provide data on the effectiveness of clean-closure practices on protecting groundwater quality in the area.
Offutt Air Force Base, south of Omaha, Nebraska, experienced major flooding during the March 2019 flood event because of the proximity of the base to the confluence of the Missouri River and nearby tributaries, which exceeded flood stages. Postflood, standing water remained through much of the year, attracting waterfowl and other birds and posing a major safety risk to aircraft. The U.S. Geological Survey, in cooperation with the U.S. Air Force, began a study in 2020 to describe the hydrologic processes that affect the persistence of standing water on Offutt Air Force Base.
Existing site data, reviewed in concert with groundwater and surface-water elevation data collected for the study, indicate varying hydrologic responses between two areas of concern (AOCs), which can be linked to differences in subsurface geology and changes in flows of the Missouri River. An inundation map indicated that standing water would be present throughout Papillion Creek Ditch in AOC 1 and would extend upstream to AOC 2 during flow events greater than 771 cubic feet per second. A U.S. Army Corps of Engineers Hydrologic Engineering Center-River Analysis System model and a flow-duration analysis were used to infer that many of the surface-water drainage problems experienced in 2019 were the result of backwater conditions caused by higher streamflows in the Missouri River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235053","collaboration":"Prepared in cooperation with the U.S. Air Force, Offutt Air Force Base","usgsCitation":"Hobza, C.M., and Strauch, K.R., 2023, Floodwater drainage assessment of Offutt Air Force Base, Nebraska, 2020–22: U.S. Geological Survey Scientific Investigations Report 2023–5053, 31 p., https://doi.org/10.3133/sir20235053.","productDescription":"Report: vii, 31 p.; Data Release; Dataset","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-138559","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":418093,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235053/full"},{"id":417735,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":417734,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KYD1CX","text":"USGS data release","linkHelpText":"Water-surface and groundwater-level elevations on and near Offutt Air Force Base, Nebraska, summer 2020 and spring 2021"},{"id":417718,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5053/images"},{"id":417717,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5053/sir20235053.XML"},{"id":417707,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5053/coverthb1.jpg"},{"id":417713,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5053/sir20235053.pdf","text":"Report","size":"5.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5053"}],"country":"United States","state":"Nebraska","otherGeospatial":"Offutt Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.09742410665552,\n 41.18555144700602\n ],\n [\n -96.09742410665552,\n 41.06019915604605\n ],\n [\n -95.87125037103594,\n 41.06019915604605\n ],\n [\n -95.87125037103594,\n 41.18555144700602\n ],\n [\n -96.09742410665552,\n 41.18555144700602\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
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The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, maintains a statewide group of stations known as the Ambient Water-Quality Monitoring Network, which includes selected streams and springs in Missouri. During water year 2021 (October 1, 2020, through September 30, 2021), the U.S. Geological Survey collected water-quality data at 72 stations: 70 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Water Quality Network stations. Four of the stations have data from additional sampling completed in cooperation with the U.S. Army Corps of Engineers. Water-quality data provided in this report include dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticide compounds. Monitoring stations have been classified based on the physiographic province or primary land use in the drainage basin or based on the unique hydrologic characteristics of the waterbodies (springs, large rivers) monitored. A summary of hydrologic conditions, including peak streamflows, monthly mean streamflows, and 7-day low flows, also is provided for representative streamgages in the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1179","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Markland, K.M., 2023, Quality of surface water in Missouri, water year 2021: U.S. Geological Survey Data Report 1179, 24 p., https://doi.org/10.3133/dr1179.","productDescription":"Report: vii, 24 p.; Dataset","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-142715","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499554,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114770.htm","linkFileType":{"id":5,"text":"html"}},{"id":418003,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1179/full"},{"id":417977,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National 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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
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/
Operation of large, multipurpose dams within the Middle Fork Willamette River Basin, Oregon, including the Fall Creek sub-basin, have disrupted natural streamflow and sediment transport regimes and fish passage along the river corridors. Documenting channel morphology, including channel planform, landforms, vegetation cover, and river channel elevations at multiple points in time spanning the 20th and early 21st centuries, is useful for characterizing net changes occurring in response to construction and operation of these dams. The U.S. Geological Survey assessed historical channel changes that occurred within the past century in response to the construction and operation of flood-control dams by evaluating planimetric datasets (from 1926 plan and profile surveys and 1936 and 2016 aerial photographs) and elevation datasets (from 1926 plan and profile surveys and 2015 light detection and ranging [lidar]). This study specifically focuses on the lower 27.3 kilometers (km) of the Middle Fork Willamette River and the lower 11.5 km of Fall Creek, or the reaches downstream from the U.S. Army Corps of Engineers Dexter Dam and Fall Creek Dam, to the confluence with Coast Fork Willamette River. Altogether, compilation and evaluation of datasets for Fall Creek and the Middle Fork Willamette River downstream from the dams provide a foundation for understanding:
Findings from this study can be used to provide historical and geomorphic context for geomorphic responses to deep reservoir drawdowns on Fall Creek Lake that mobilize reservoir sediment downstream and informing other restoration and river-management activities; this report summarizes one component of a larger research effort to document the magnitude and spatial distribution of geomorphic responses to sediment releases from draining Fall Creek Lake.
As of 2016, the modern Fall Creek flows through a narrow, semi-alluvial channel that efficiently conveys water and sediment at typical streamflows downstream from Fall Creek Dam. This channel planform, including the positions and distributions of bars and secondary water features (side channels, alcoves, and ponds), generally reflects pre-dam conditions in 1936, suggesting relatively modest morphological adjustments resulted from reductions in sediment supply and alterations to peak streamflow after dam construction. The most substantial morphologic change detected over this period was a reduction in unvegetated gravel bars.
As of 2016, the modern Middle Fork Willamette River is a large, gravel-bed river that, despite substantial transformations in channel morphology and reduction in lateral dynamism following the construction of multiple upstream dams, remains a dominantly alluvial river. Prior to dam construction in 1926 and 1936, the reaches of the Middle Fork Willamette River downstream from Dexter Dam were laterally active with multi-thread and single-thread channels flanked by large, shifting gravel bars. Since streamflow regulation and other channel modifications in the mid-20th century, these reaches have become less laterally active and encompass a narrower floodplain corridor as abundant former gravel bars were converted to low-elevation floodplains colonized by young, dense forests. The Middle Fork Willamette River downstream from Dexter Dam has remained mostly vertically stable between 1926 and 2015, although localized segments possibly decreased in elevation as much as 2.3 meters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235048","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Keith, M.K., Wallick, J.R., Gordon, G.W., and Bervid, H.D., 2023, Historical changes to channel planform and bed elevations downstream from dams along Fall Creek and Middle Fork Willamette River, Oregon, 1926–2016: U.S. Geological Survey Scientific Investigations Report 2023–5048, 34 p., https://doi.org/10.3133/sir20235048.","productDescription":"Report: viii, 34 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-136568","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":500925,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114772.htm","linkFileType":{"id":5,"text":"html"}},{"id":417882,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5048/sir20235048.XML"},{"id":417881,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5048/images"},{"id":417880,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9THIZD6","text":"USGS data release","description":"USGS data release.","linkHelpText":"Fall Creek and Middle Fork Willamette Geomorphic Mapping Geodatabase"},{"id":417877,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5048/coverthb.jpg"},{"id":417878,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5048/sir20235048.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Oregon","otherGeospatial":"Fall Creek, Middle Fork Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123,\n 44\n ],\n [\n -123,\n 43.916667\n ],\n [\n -122.75,\n 43.916667\n ],\n [\n -122.75,\n 44\n ],\n [\n -123,\n 44\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The U.S. Geological Survey, in cooperation with the Suffolk County Water Authority, evaluated the groundwater-flow characteristics and aquifer properties of the North Magee Street well field north of the village of Southampton, New York. Characteristics and properties included groundwater-flow direction, potential groundwater-contributing areas to the well field production wells, and aquifer transmissivity and storage. The groundwater flow and aquifer properties were also evaluated to allow Suffolk County Water Authority to better assess the potential source of dissolved halocarbons (refrigerants, such as chlorofluorocarbons).
The well field production wells are screened in the upper glacial aquifer and an observation well is screened in the Magothy aquifer. Based on depth and available logs, groundwater from wells screened in the upper glacial aquifer was classified as under water-table (unconfined) conditions, and groundwater from wells screened in the Magothy aquifer was classified as being under semiconfined conditions.
Groundwater flows radially to the well field during production and in a northwesterly direction under the effect of the regional flow regime. A previously published particle tracking analysis identified the following recharge contributing areas nearby the well field: (1) contributing areas to surface-water bodies of the Peconic Estuary, (2) contributing areas to surface-water bodies of the South Shore Estuary Reserve, (3) a contributing area to the Atlantic Ocean, and (4) a contributing area to another Suffolk County Water Authority well field. Five other pumping well contributing areas were identified within the study area, including those of various wells pumped for golf-course irrigation.
Analysis of drawdown and recovery data collected during the multiple-well aquifer test, through the application of a Neuman analytical model, provided estimates of upper glacial aquifer characteristics and properties. Inclusion of lateral aquifer boundaries was not necessary for the analysis to result in satisfactory matches with the observed water-level responses. Aquifer transmissivity was estimated to be 170,000 feet squared per day. Storativity was estimated to be 0.02 (dimensionless), and specific yield was estimated to be 0.08 (dimensionless), consistent with the inferred degree of confinement and well field characteristics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231028","collaboration":"Prepared in cooperation with the Suffolk County Water Authority","usgsCitation":"Misut, P.E., 2023, Analysis of aquifer framework and properties, North Magee Street well field, Southampton, New York: U.S. Geological Survey Open-File Report 2023–1028, 14 p., https://doi.org/10.3133/ofr20231028.","productDescription":"Report: iv, 14 p.; Dataset","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124210","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":499775,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114761.htm","linkFileType":{"id":5,"text":"html"}},{"id":417746,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":417745,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1028/images/"},{"id":417744,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1028/ofr20231028.XML"},{"id":417743,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231028/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1028"},{"id":417742,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1028/ofr20231028.pdf","text":"Report","size":"2.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1028"},{"id":417741,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1028/coverthb.jpg"}],"country":"United States","state":"New York","city":"Southampton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.43897301957882,\n 40.91839299249426\n ],\n [\n -72.43897301957882,\n 40.87699855750361\n ],\n [\n -72.37328061868494,\n 40.87699855750361\n ],\n [\n -72.37328061868494,\n 40.91839299249426\n ],\n [\n -72.43897301957882,\n 40.91839299249426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The Trinity River is managed in two sections: (1) the upper 64-kilometer (km) “restoration reach” downstream from Lewiston Dam and (2) the 120-km lower Trinity River downstream from the restoration reach. The Stream Salmonid Simulator (S3) has been previously constructed and calibrated for the restoration reach. In this report, we extended and parameterized S3 for the 120-km section of the lower Trinity River to the confluence with the Klamath River and then to the Pacific Ocean in northern California.
S3 is a deterministic life-stage structured-population model that tracks daily growth, movement, and survival of juvenile salmon. A key theme of the model is that river discharge affects habitat availability and capacity, which in turn drives density-dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily timeseries of discharge, water temperature, and useable habitat area or carrying capacity. In turn, the physical characteristics of each habitat unit and the number of fish occupying each unit drive (1) survival and growth within each habitat unit and (2) movement of fish among habitat units.
The physical template of the Trinity River was formed by classifying the river into 910 meso-habitat units that were designated into runs, riffles, or pools. For each habitat unit, we developed a timeseries of daily discharge, water temperature, amount of available spawning habitat, and fry and parr carrying capacity. Capacity timeseries were constructed using state-of-the-art models of spatially explicit hydrodynamics and quantitative fish habitat relationships developed for the Trinity River. These variables were then used to drive population dynamics such as egg maturation and survival, and in turn, juvenile movement, growth, and survival.
We estimated key movement and survival parameters by calibrating the model to 12 years (2007–18) of weekly juvenile abundance estimates from two rotary screw traps: (1) the Pear Tree trap near the downstream end of the restoration reach and (2) the Willow Creek trap site is about 40.2 km upriver from the Trinity River’s confluence with the Klamath River. The calibration consisted of replicating historical conditions as closely as possible (for example: flow, temperature, spawner abundance, spawning location and timing, and hatchery releases), and then running the model to predict weekly abundance passing the trap location. We also evaluated four alternative model structures that included either no density-dependence, density-independent movement and survival, density-dependent survival, or density-dependent movement. Akaike information criterion model selection was used to evaluate the strength of evidence for alternative model structures to simulate the observed abundance estimates.
Model selection supported the conclusion that the fully density-dependent model and density-dependent survival model was better supported by the data than the no density-dependence or density-dependent movement model. Because density-dependent movement was favored in past evaluations, we focus on the results from the fully density-dependent model. Parameter estimates from this model indicated that fry were less likely than parr to move downstream and that fry moved slower. Fry had a lower daily survival probability than parr. In contrast, hatchery fish had the highest probability of movement and the lowest daily survival probability.
Fitting the model to both traps individually enabled us to independently compare the fit and performance of S3 at simulating fish abundance, timing, and growth of juvenile salmon in the upper restoration reach and lower Trinity River. We obtained a better fit to the data at the Willow Creek trap site than we obtained at the Pear Tree trap site, regardless of whether we fit the model to the abundances at the Pear Tree trap or Willow Creek trap. This better fit was surprising given that the S3 input data for the upper restoration reach required fewer assumptions than fitting to the Willow Creek trap site that is farther down river. Fitting S3 to weekly abundances at the Willow Creek trap site required making assumptions about (1) extrapolating capacity-flow relationships to unmeasured habitat units; (2) spatially allocating spawners within the lower Trinity River; and (3) approximating the abundance, timing, and size of juveniles entering from tributaries. The model provided better fit to the data at the Willow Creek trap site. In the weekly abundance estimates, in relation to the S3 simulated abundances, several migration years’ (2011, 2015–17) weekly abundance estimates appeared truncated and were near or at peak annual abundances in January, suggesting that a large fraction of juveniles was migrating as early as December at the Pear Tree trap site. Some early life dynamics may not be currently incorporated into S3. For example, the estimation of abundance at the Pear Tree trap may be biased because of size selectivity. Knowing about selectivity at the Pear Tree trap could greatly improve S3’s ability to predict weekly and peak abundances each year.
Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Groundwater is important as a drinking-water source and for maintaining base flow in rivers, streams, and lakes. Groundwater quality can be predicted, in part, by its residence time in the subsurface, but the residence-time distribution cannot be measured directly and must be inferred from models. This report compares residence-time distributions from four areas where groundwater flow and travel time were simulated with conventional simulation-inset models (IMs) and with a new automated model-construction method called general simulation models (GSMs). The comparison provides an opportunity to explore controls on travel time and improve the methods used in the creation of GSMs. These models can be useful for three main-use cases: (1) rapid testing of relationships that govern groundwater flow and age, (2) generation of consistent examples for training a machine-learning metamodel, and (3) serving as a starting point for more detailed models.
Comparison of the GSMs to IMs indicated a qualified pattern of agreement for residence-time distributions as indicated by the Nash-Sutcliffe efficiency and Spearman’s correlation coefficient. The agreement was best for the median values of the simulated residence times in young fractions of groundwater (defined as the fractions of groundwater in samples less than 65 years old) at the scale of the eight-digit hydrologic-unit code. Generally, the median values of the young fractions in the IMs were correlated with the median values from the GSMs. The relative trends across the four areas also were similar for the other residence-time metrics. The medians of residence-time metrics at finer scales show a fair degree of scatter. The GSM results compared most poorly for median travel times in the older fraction of groundwater (older than 65 years).
The GSM approach is intended as a flexible framework for developing models that can be useful individually as screening tools or collectively to support projects in statistical learning. Although one set of GSM algorithms was presented here, the approach can accommodate many types of data and also different categories of prior information. Comparison of GSMs and IMs suggests ways in which the GSMs, while remaining easy to construct and calibrate, can be improved for estimating groundwater travel times. IMs do not yield exact travel times, and matching GSMs to IMs does not guarantee an improvement; however, IMs provide a convenient benchmark against which to explore relations between physical characteristics of watersheds and the distribution of travel times within them.
This effort was undertaken as part of the National Water Quality Program of the U.S. Geological Survey to assist in determining the susceptibility of groundwater in glacial aquifers to a variety of natural and anthropogenic contaminants.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215142","programNote":"National Water Quality Program","usgsCitation":"Starn, J.J., Kauffman, L.J., and Feinstein, D.T., 2023, Groundwater residence times in glacial aquifers—A new general simulation-model approach compared to conventional inset models: U.S. Geological Survey Scientific Investigations Report 2021–5142, 37 p., https://doi.org/10.3133/sir20215142.","productDescription":"Report: v, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112499","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":500447,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114759.htm","linkFileType":{"id":5,"text":"html"}},{"id":413862,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5142/images/"},{"id":413858,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5142/coverthb.jpg"},{"id":413859,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5142/sir20215142.pdf","text":"Report","size":"6.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5142"},{"id":413861,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5142/sir20215142.XML"},{"id":413863,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HS83JL","text":"USGS data release","linkHelpText":"MODPATH-NWT and MODPATH6 models used to compare a new general simulation model approach with a conventional inset model approach for groundwater residence time in glacial aquifers"},{"id":413860,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215142/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5142"}],"country":"United States","state":"Illinois, Indiana, Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.5,\n 46\n ],\n [\n -89,\n 46\n ],\n [\n -89,\n 41.5\n ],\n [\n -84.5,\n 41.5\n ],\n [\n -84.5,\n 46\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Selenium in aquatic ecosystems of the lower Gunnison River Basin in Colorado is affecting the recovery of populations of endangered, native fish species. Dietary exposure is the primary pathway for bioaccumulation of selenium in fish, and particulate selenium can be consumed directly by fish or by the invertebrates on which fish feed. Although selenium can be incorporated into particulate matter via biogeochemical processes, particulate selenium can also enter aquatic ecosystems of the lower Gunnison River Basin from sediments derived from the selenium-rich Mancos Shale. The U.S. Geological Survey, in cooperation with the Colorado Water Conservation Board, conducted this study during 2018–19 to identify sources of selenium-rich suspended sediments from two watersheds underlain by Mancos Shale: Loutsenhizer Arroyo and Sunflower Drain, which is a locally known agricultural drainage near the municipality of Delta, Colorado.
A multipronged approach (fieldwork, laboratory work, and computer modeling) referred to as “sediment fingerprinting” was used to evaluate sources of suspended sediments in the streams flowing out of the two studied watersheds. Four potential source types for suspended sediments were identified and sampled (using soil plugs) within the watersheds: rangelands, agricultural fields, arroyo walls, and streambanks. The sediment fingerprinting approach used elemental concentrations and naturally occurring fallout radionuclides as tracers to apportion percent contributions from the four source types of suspended sediments found in streamflow from both watersheds.
To determine the dominant sources of suspended sediment in streamflow from both watersheds, a mathematical “unmixing” model was used. Unmixing models apportion source percentages to samples of material in which those sources are mixed. These models used elemental and isotopic data in the suspended sediments to unmix them into proportional contributions from source types. The results indicated that arroyo walls and streambanks generally dominated as sources of the suspended sediment. Arroyo walls and streambanks were channel-adjacent sources, with sediments mobilized by water flowing within the stream channel. These sources accounted for greater than 50 percent of suspended sediment in all but one sample and accounted for 100 percent of suspended sediment in 5 of the 11 samples collected. Rangeland and agricultural field sources were located in uplands outside of stream channels and were detected more often during the non-irrigation season. Rangeland and agricultural field sources each were found in 5 of the 11 samples collected. Concentrations of selenium in sediment-source samples were comparatively greater in streambanks and lower in rangelands, with agricultural fields and arroyo walls being intermediate. As a result, source apportionments for particulate selenium skewed towards sources adjacent to stream channels more than for suspended sediments. Water imports for irrigation have changed the hydrology of the watersheds, and a notable fraction of imported water passes through the watersheds rapidly. The rapid flowthrough water during the irrigation season likely contributes heavily to sediment erosion and transport in Loutsenhizer Arroyo and Sunflower Drain, particularly from channel-adjacent sources of sediment. Decreases in irrigation season streamflow, at least in Loutsenhizer Arroyo, may have decreased sediment erosion and transport during the 2018–20 irrigation seasons compared to the 2015–17 seasons.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235056","collaboration":"Prepared in cooperation with the Colorado Water Conservation Board","usgsCitation":"Bern, C.R., Williams, C.A., and Smith, C.G., 2023, Source contributions to suspended sediment and particulate selenium export from the Loutsenhizer Arroyo and Sunflower Drain watersheds in Colorado: U.S. Geological Survey Scientific Investigations Report 2023–5056, 32 p., https://doi.org/10.3133/sir20235056.","productDescription":"Report: vii, 32 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-132717","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":485917,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114745.htm","linkFileType":{"id":5,"text":"html"}},{"id":417883,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235056/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5056"},{"id":417619,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5056/sir20235056.xml"},{"id":417618,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5056/images"},{"id":417612,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99EZZJK","text":"USGS data release","linkHelpText":"Geochemical and fallout radionuclide data for sediment source fingerprinting studies of the Loutsenhizer Arroyo and Sunflower Drain watersheds in western Colorado"},{"id":417611,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5056/sir20235056.pdf","text":"Report","size":"3.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5056"},{"id":417610,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5056/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Loutsenhizer Arroyo Watershed, Sunflower Drain Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.2032088457371,\n 38.871468271392075\n ],\n [\n -108.2032088457371,\n 38.38029358037457\n ],\n [\n -107.27351004163626,\n 38.38029358037457\n ],\n [\n -107.27351004163626,\n 38.871468271392075\n ],\n [\n -108.2032088457371,\n 38.871468271392075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
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Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 15 bridges at 10 highway crossings of the Missouri and Mississippi Rivers near Washington, Louisiana, and St. Louis, Missouri, on August 3–10, 2020. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches about 1,640 to 1,970 feet longitudinally and generally extending laterally across the active channel from bank to bank during moderate flood-flow conditions. These surveys provided channel geometry and hydraulic conditions at the time of the surveys and provided characteristics of scour holes that may be useful in developing predictive guidelines or equations for computing potential scour depth. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood-flow assessment of the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every in-channel pier. Scour holes were present at most piers for which bathymetry could be obtained, except those on banks or surrounded by riprap. All the bridge sites in this study were previously surveyed and documented in previous studies, including the two new bridge structures at Louisiana and Washington (structures A8141 and A8504, sites 22 and 32, respectively). Comparisons between bathymetric surfaces from the previous surveys and those of the current (2020) study do not indicate any consistent correlation in channel-bed elevations with streamflow conditions. The comparisons of the 2020 surveys to two previous surveys at the new bridge structure A8141 at Washington (site 22) resulted in net erosion of the channel bed in both comparisons, despite the 2020 streamflow being less than either previous survey. Alternatively, there was a net gain of sediment at new bridge structure A8504 at Louisiana (site 32) between 2014 and 2020, which was the most substantial increase in the surveys detailed in this report; substantially less flow in 2020 than in 2014 or changes to the channel and spur dikes near the bridge may have contributed to the observed sediment gain.
Pier size, nose shape, and skew to approach flow had a substantial effect on the size of the scour hole observed at a given pier. Larger and deeper scour holes were present at piers with wide or blunt noses caused by exposed footings, seal courses, or caissons. When a pier was skewed to primary approach flow, the scour hole was generally deeper and larger than at a similar pier without skew; however, the shape of the scour hole near skewed piers in this study generally was longer and deeper on the leeward side, contrary to the general shape of scour holes for skewed piers. However, this phenomenon has been observed historically at these sites, and likely is exacerbated by debris rafts or other turbulence-inducing features near the atypical scour holes. A substantial scour hole was observed near pier 11 of structure A6500 (site 33), which was deeper than in the 2016 survey. The scour holes observed at pier 17 of structure L0561 (site 25) and piers 3 and 4 of structure A1500 (site 34) also were slightly deeper and wider in 2020 than in 2016. At new bridge structures A8141 at Washington (site 22) and A8504 at Louisiana (site 32), the smaller cross-sectional area and configuration of the piers of the new bridges resulted in substantially less scour than with the wider old piers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235050","collaboration":"Prepared in cooperation with Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2023, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020 (ver. 1.1, June 2023): U.S. Geological Survey Scientific Investigations Report 2023–5050, 129 p., https://doi.org/10.3133/sir20235050.","productDescription":"Report: xii, 129 p.; 4 Data Releases","numberOfPages":"146","onlineOnly":"Y","ipdsId":"IP-137672","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":500928,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114743.htm","linkFileType":{"id":5,"text":"html"}},{"id":417650,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2023/5050/versionHist.txt","text":"Version History","size":"1.19 kB","linkFileType":{"id":2,"text":"txt"}},{"id":417532,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5050/images"},{"id":417434,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F04JC5","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, August 3–10, 2020"},{"id":417432,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDI9YF","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, December 2008 through August 2018"},{"id":417431,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94M4US7","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri River between Kansas City and St. Louis, Missouri, January 2010 through May 2017"},{"id":417429,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71C1VCC","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers near St. Louis, Missouri, October 2008 through May 2016"},{"id":417428,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5050/sir20235050.XML","linkFileType":{"id":8,"text":"xml"}},{"id":417427,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5050/sir20235050.pdf","text":"Report","size":"26.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5050"},{"id":417426,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5050/coverthb2.jpg"},{"id":417651,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235050/full"}],"country":"United States","state":"Illinois, Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.7316541640954,\n 39.115075665885314\n ],\n [\n -90.7316541640954,\n 38.326846254515004\n ],\n [\n -90.02883149031791,\n 38.326846254515004\n ],\n [\n -90.02883149031791,\n 39.115075665885314\n ],\n [\n -90.7316541640954,\n 39.115075665885314\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: May 25, 2023; Version 1.1: June 1, 2023","contact":"Director, Central Midwest Water Science Center
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The winter rainy season of 2016–2017 brought abundant rainfall to the State of California and to the San Francisco Bay region. In January and February of 2017, intense rainfall from strong winter storms saturated soils in the region and triggered thousands of shallow landslides. The highest concentration of these landslides was in the eastern part of the bay region, where landslides in the hills east of the Cities of Richmond, Berkeley, Oakland, Hayward, and Fremont (see main map for locations) damaged homes, displaced a major electrical transmission-line tower, and blocked several heavily traveled roadways.
This map shows 8,928 landslides manually mapped from rectified high-resolution (0.25-meter [m]) satellite imagery (March 11, 2017, from Google Earth) in a three-dimensional geographic information system (GIS) framework. The map area encompasses approximately 1,050 square kilometers (km2), bounded by the Carquinez Strait and San Francisco Bay to the north and west, respectively, and extending to the Interstate Highway 680 corridor to the south and east. Individual landslides were mapped as polygons, but for ease of display, they are shown here as points denoting the highest elevation of each landslide headscarp. The greatest calculated landslide concentration (measured as the total number of landslides per unit area) exceeded 80 landslides per 0.25 km2 in the hills east of the City of Berkeley. This zone is illustrated on the map in red, a color which denotes areas of greater than or equal to 30 landslides per 0.25 km2. The source area and deposit polygon datasets are available as a data release (Corbett and Collins, 2023). Complementary field investigations at more than 150 landslides of those displayed here indicate that most mapped landslides are shallow (less than 1 m deep) debris slides and debris flows. Although most landslides occurred in undeveloped areas (for example, parks and open space), about 1,400 landslides, or 16 percent of the total number, affected structures or infrastructure (including paved and dirt roads) in some way. The inset figures (figs. 1–5) show photographs taken in January and February 2017 that depict some areas of damage that occurred during and after the storms; the locations of where these photographs were taken are shown on the map.
In the San Francisco Bay region, landslides usually take place each year during the winter rainy season, but widespread, intense rainfall events such as those whose effects are depicted here typically occur as a result of considerably above-average precipitation conditions. Although the landslides of early 2017 are not the most damaging to affect the region historically, they are still a potent reminder of the potential for landslide hazards present in the San Francisco Bay region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3503","usgsCitation":"Corbett, S.C., and Collins, B.D., 2023, Landslides triggered by the 2016–2017 storm season, eastern San Francisco Bay region, California: U.S. Geological Survey Scientific Investigations Map 3503, scale 1:75,000, https://doi.org/10.3133/sim3503.","productDescription":"1 Plate: 37.12 x 44.88 inches; Data Release","ipdsId":"IP-126449","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":417161,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3503/sim3503_sheet.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417160,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3503/covrthb.jpg"},{"id":417163,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98MVEGI","text":"Mapped polygons of landslides triggered by the 2016–2017 storm season, eastern San Francisco Bay region, California","description":"Corbett, S.C., and Collins, B.D., 2023, Mapped polygons of landslides triggered by the 2016–2017 storm season, eastern San Francisco Bay region, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9XPA7UC."},{"id":500209,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114739.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"eastern San Francisco Bay region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.86773652737136,\n 37.59196580130809\n ],\n [\n -121.95011125798024,\n 37.760393221950594\n ],\n [\n -122.01875686682075,\n 37.85530428686653\n ],\n [\n -122.03935054947307,\n 38.00420363734628\n ],\n [\n -122.06680879300936,\n 38.03935395235649\n ],\n [\n -122.23842281511067,\n 38.06908335518102\n ],\n [\n -122.35855263058198,\n 38.006908106324346\n ],\n [\n -122.43406280030648,\n 37.96362463343144\n ],\n [\n -122.37571403279212,\n 37.88781713463695\n ],\n [\n -122.23842281511067,\n 37.790235557897105\n ],\n [\n -122.07024107345114,\n 37.64905661408321\n ],\n [\n -121.95011125798024,\n 37.551159813602936\n ],\n [\n -121.86773652737136,\n 37.59196580130809\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
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This report presents a reference flow network for the conterminous United States that is built from the best available information from the U.S. Geological Survey, the National Oceanic and Atmospheric Administration National Weather Service, and the U.S. Environmental Protection Agency. The work is intended to support durable data integration and reproducibility. Originating from the National Hydrography Dataset Plus (NHDPlus) V2.1, the reference flow network incorporates network connectivity enhancements from federal agency efforts. After incorporating these network improvements, many original NHDPlus attributes were regenerated to enable network navigation and related operations. After introducing the motivation and background for this work, this report describes the attribute generation workflow and data quality checks that were performed in preparation of the dataset. The reference flow network follows the NHDPlus data model and is described using terms defined in the Mainstem and Drainage Basin logical model and WaterML2 Part3: Surface Hydrology Features conceptual model.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2023.105726","usgsCitation":"Blodgett, D.L., Johnson, J., and Bock, A.R., 2023, Generating a reference flow network with improved connectivity to support durable data integration and reproducibility in the coterminous US: Environmental Modelling and Software, v. 165, 105726, 10 p., https://doi.org/10.1016/j.envsoft.2023.105726.","productDescription":"105726, 10 p.","ipdsId":"IP-148062","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":443438,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2023.105726","text":"Publisher Index Page"},{"id":417290,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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Division","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, J. Michael","contributorId":304963,"corporation":false,"usgs":false,"family":"Johnson","given":"J. Michael","affiliations":[{"id":66193,"text":"NOAA-NWS-OWP","active":true,"usgs":false}],"preferred":false,"id":873361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873362,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243696,"text":"sir20235027 - 2023 - Evaluating drivers of hydrology, water quality, and benthic macroinvertebrates in streams of Fairfax County, Virginia, 2007–18","interactions":[],"lastModifiedDate":"2026-03-06T20:51:31.369117","indexId":"sir20235027","displayToPublicDate":"2023-05-18T10:56:00","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":"2023-5027","displayTitle":"Evaluating Drivers of Hydrology, Water Quality, and Benthic Macroinvertebrates in Streams of Fairfax County, Virginia, 2007–18","title":"Evaluating drivers of hydrology, water quality, and benthic macroinvertebrates in streams of Fairfax County, Virginia, 2007–18","docAbstract":"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
The central Salem River in New Jersey is subject to periods of water-quality impairment, marked by elevated concentrations of phosphorus and chlorophyll-a, and low concentrations of and large diurnal swings in concentrations of dissolved oxygen. These seasonal eutrophic conditions are controlling factors for water quality in lower reaches, where the river is more lacustrine than in upper reaches, as a result of downstream damming. This biological productivity is supported by nutrient wash-off from agricultural areas in the surrounding watershed. To investigate this impairment, flow measurement and water-quality sampling were conducted during 2007–08 in support of development of a one-dimensional surface-water-quality model that simulates nutrient cycling and transformation processes.
The U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, used the U.S. Environmental Protection Agency Water Quality Analysis Simulation Program (WASP) to develop a receiving-water-quality model of the central Salem River between Woodstown and Deepwater, New Jersey, from April 2007 to October 2008. The main-stem river and largest tributary were simulated. In the flow model, kinematic wave flow is used to simulate flow in upper reaches and ponded weir flow is used to simulate flow in lower reaches. The water-quality model makes use of a mass-balance equation to simulate the fate and transport of nutrients, phytoplankton chlorophyll-a, dissolved oxygen, and oxygen demands (an indicator rather than a substance) in the river. Model input included channel characteristics, boundary conditions for flow and water quality, environmental parameters, vertical dispersion coefficients, settling rates, and kinetic constants. Inputs were estimated where field data were lacking, notably for tributary flows and nutrient loads.
The model was calibrated to observed flow variables and concentrations of dissolved oxygen, chlorophyll-a, and nutrients at sampling locations, with emphasis on growing-season conditions. Calibration was achieved through graphical and statistical comparison of simulated results to observed data. Sensitivity analyses were performed, and model limitations and applicability were evaluated. Simulated results closely matched observed data in most cases, although some were overpredicted slightly. The most important causes of overprediction were estimated tributary flows for the flow model and estimated tributary watershed loads for the water-quality model. Calibration of dissolved-oxygen concentrations was closer, and predicted diurnal variations were consistent with high algal photosynthesis/respiration, although lack of continuous dissolved-oxygen data precluded verifying these predictions. A similar caveat applies to predicted diurnal variations in chlorophyll-a. Simulated limitations on algal growth were consistent with those based on observed data and indicated phosphorus was the main limiting nutrient, except during certain periods when nitrogen was limiting.
Two water-quality management scenarios were simulated with the model to assess the effect of point- and nonpoint-source nutrient reductions on water-quality conditions in the river. Scenarios involved (1) a return of watershed land use to predevelopment natural conditions and (2) an extreme reduction in nutrient input. Although the extreme-nutrient-reduction scenario yielded improvements in water quality, the natural-conditions scenario yielded the largest improvements as indicated by minimal violations of surface-water-quality standards or thresholds. However, years may be needed to attain the full benefit of these management scenarios as a result of accumulation of phosphorus and organic carbon in riverbed sediments in lacustrine reaches. The results of this study indicate that the quality of water in the central Salem River will improve if management policies that mitigate the effects of nutrient-loading practices in the watershed, particularly those related to agriculture, are implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225047","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Spitz, F.J., and DePaul, V.T., 2023, Simulation of flow and eutrophication in the central Salem River, New Jersey: U.S. Geological Survey Scientific Investigations Report 2022–5047, 72 p., https://doi.org/10.3133/sir20225047.","productDescription":"Report: x, 72 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109225","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":500449,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114735.htm","linkFileType":{"id":5,"text":"html"}},{"id":417027,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78G8JPJ","text":"USGS data release","linkHelpText":"WASP model used to simulate flow and eutrophication in the central Salem River, New Jersey"},{"id":417026,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5047/images/"},{"id":417025,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.XML"},{"id":417024,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225047/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5047"},{"id":417023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5047"},{"id":417022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5047/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Central Salem River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.51261142665348,\n 39.66506027345514\n ],\n [\n -75.13011677160785,\n 39.493754929673486\n ],\n [\n -75.01123329774249,\n 39.637202213256444\n ],\n [\n -75.41569555121957,\n 39.76545497451639\n ],\n [\n -75.51261142665348,\n 39.66506027345514\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
During the economic boom of the mid part of the first decade of the 2000s in northwestern Nevada, municipal and housing growth increased use of the water resources of this semi-arid region. In 2008, when the economy slowed, new housing development stopped, and immediate pressure on groundwater resources abated. The U.S. Geological Survey, in cooperation with the Bureau of Reclamation, began a hydrogeologic study of the middle Carson River Basin. The first half of the study reviewed and synthesized previous geologic studies and contributed new datasets that served as a foundation for a three-dimensional, transient numerical model of groundwater and surface-water flow for the middle Carson River Basin extending from Eagle Valley to Churchill Valley. The model can be used to evaluate the effects of proposed alternative management strategies on groundwater sustainability, flows in the Carson River, and routine operation of Lahontan Reservoir and can also provide a basis for basin-wide investigations seeking to quantitatively evaluate the effects of climate change or yet-to-be-determined alternative management strategies.
The middle Carson model was constructed using the U.S. Geological Survey groundwater modeling software MODFLOW-NWT. MODFLOW is widely used groundwater modeling software and is well-suited for evaluating groundwater and surface-water interactions. The model uses 550-feet square grid cells that align with the previously published model for Carson Valley (adjacent upstream valley). Six grid layers with more finely resolved vertical resolution near the perimeter of the active model domain and near surface-water features, compared to other areas of the active model domain, hone the simulated groundwater and surface-water exchanges. In addition to simulating groundwater and surface-water interaction, crop and phreatophyte evapotranspiration, lake evaporation, mountain-front recharge, recharge from irrigation return flows, and groundwater pumping are also simulated. Surface-water flow entering the model domain, including the Carson River, tributary inflow from perennial streams in Eagle Valley, and trans-basin imports through the Truckee Canal (surface water diverted from the Truckee River) are specified according to U.S. Geological Survey streamgage records. Groundwater pumpage and surface-water diversions to 10 agricultural ditches and the managed release from Lahontan Reservoir, at the end of the middle Carson River Basin, are specified according to water-manager records.
The model simulation period extended from 2000 through 2010 (January 1, 2000, to December 31, 2010) using 574 weekly stress periods, with a single steady-state stress period at the beginning of the simulation that establishes initial conditions by approximating average conditions during the transient simulation period. All available observations for this period were used during the model calibration process, performed using automated parameter-estimation software. Calibration targets included observations of groundwater elevations in wells, streamflow, differences in observed streamflow between successive streamgages and actual evapotranspiration from irrigated lands. Among all 5,296 simulated and observed groundwater level pairs, the mean error was 1.42 feet; the mean absolute error, 7.71 feet; and the percent bias was −0.1 percent.
Three alternative management scenarios, run using the entire period of analysis (2000–10), were simulated to improve understanding of the potential effects of (1) loss of irrigated agricultural lands following conversion of water-rights to municipal groundwater rights; (2) reclaiming treated wastewater with induction wells; and (3) exercising permitted but under-utilized groundwater rights. Scenarios 2 and 3 were further explored using two and four subscenarios, respectively. Simulated scenario results ranged from having little effect on the groundwater system relative to a baseline simulation to having spatially extensive and large groundwater-level declines (10 to 20 feet) compared to the baseline simulation. None of the simulated scenarios increased delivery of river flows to Lahontan Reservoir. On the contrary, one of the subscenarios under alternative management scenario 3 led to surface-water delivery shortfalls of more than 10,000 acre-feet per year.
Future model improvements may include an extension of the model simulation period backward and forward in time and directly linking it to the upstream Carson Valley groundwater model. Furthermore, converting this MODFLOW model to a GSFLOW model, which fully integrates groundwater and surface-water flows including precipitation runoff and infiltration, may provide an improved tool for comprehensive management of water-resources in the middle Carson River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235008","collaboration":"Prepared in cooperation withv the Bureau of Reclamation","usgsCitation":"Morway, E.D., Buto, S.G., Niswonger, R.G., and Huntington, J.L., 2023, Assessing potential effects of changes in water use in the middle Carson River Basin with a numerical groundwater-flow model, Eagle, Dayton, and Churchill Valleys, west-central Nevada: U.S. Geological Survey Scientific Investigations Report 2023–5008, 112 p., https://doi.org/10.3133/sir20235008.","productDescription":"Report: xiii, 112 p.; 3 Data Releases","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-034336","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":416912,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D3XO1U","text":"Data for the report assessing potential effects of changes in water use in the middle Carson River Basin with a numerical groundwater-flow model, Eagle, Dayton, and Churchill Valleys, west-central Nevada","description":"Morway, E.D., Buto, S.G., and Medina, R.L., 2023, Data for the report assessing potential effects of changes in water use in the middle Carson River Basin with a numerical groundwater-flow model, Eagle, Dayton, and Churchill Valleys, west-central Nevada: U.S. Geological Survey data release, https://doi.org/10.5066/P9D3XO1U."},{"id":416913,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N9FNQZ","text":"MODFLOW-NWT model used to simulate potential effects of changes in water use in the middle Carson River Basin, Eagle, Dayton, and Churchill Valleys, west-central, Nevada","description":"Morway, E.D., Niswonger, R.G., and Buto, S.G., 2023, MODFLOW-NWT model used to simulate potential effects of changes in water use in the middle Carson River Basin, Eagle, Dayton, and Churchill Valleys, west-central, Nevada: U.S. Geological Survey data release, https://doi.org/10.5066/P9N9FNQZ."},{"id":416907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5008/covrthb.jpg"},{"id":416908,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5008/sir20235008.pdf","text":"Report","size":"18 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416909,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5008/sir20235008.xml"},{"id":416910,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5008/images"},{"id":416911,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235008/full"},{"id":416921,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P5LJ3P","text":"Data for the report Geologic Framework and Hydrogeology of the middle Carson River basin, Eagle, Dayton, and Churchill Valleys, West-Central Nevada","description":"Maurer, D.K., and Medina, R.L., 2020, Data for the report Geologic Framework and Hydrogeology of the middle Carson River basin, Eagle, Dayton, and Churchill Valleys, West-Central Nevada: U.S. Geological Survey data release, https://doi.org/10.5066/P9P5LJ3P."}],"country":"United States","state":"Nevada","otherGeospatial":"Middle Carson River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120,\n 40.5\n ],\n [\n -120,\n 38\n ],\n [\n -118,\n 38\n ],\n [\n -118,\n 40.5\n ],\n [\n -120,\n 40.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
The Rio San Jose Integrated Hydrologic Model (RSJIHM) was developed to provide a tool for analyzing the hydrologic system response to historical water use and potential changes in water supplies and demands in the Rio San Jose Basin. The study area encompasses about 6,300 square miles in west-central New Mexico and includes the communities of Grants, Bluewater, and San Rafael and three Native American Tribal lands: the Acoma and Laguna Pueblos and the Navajo Nation. Perennial surface water features are sparse in the study area and most water resources consist of groundwater pumped from sedimentary and basalt aquifers.
Calibration of the RSJIHM was performed using PEST++ (version 4.3.20) and BeoPEST (version 13.6). Model parameter values were adjusted during calibration to fit model simulated values to the measured or estimated values for several observation groups: (1) solar radiation, (2) potential evapotranspiration, (3) actual evapotranspiration, (4) precipitation and minimum and maximum air temperature, (5) snow water equivalent, (6) snow-covered area, (7) streamflow, (8) hydraulic head, (9) springflow at Ojo del Gallo, (10) springflow at Horace Springs, (11) surface-water releases from Bluewater Lake, and (12) surface-water diversions for irrigation within the Bluewater-Toltec Irrigation District.
The simulated average annual hydrologic budget from 1950 through 2018 indicated that the majority (greater than 98 percent) of precipitation within the basin was consumed by evapotranspiration, leaving 1.2 percent to recharge the groundwater system, 0.47 percent to direct runoff to streams, and 0.20 percent to infiltrate the soil zone and interflow to streams. The average annual recharge to the groundwater system and runoff to streams simulated by the RSJIHM was about 28,000 and 11,000 acre-feet, respectively. The RSJIHM simulated about 590,000 acre-feet of cumulative aquifer storage depletion from 1950 through 2018.
Additional work that could improve the simulation capability of the RSJIHM includes (1) further data collection (streamflow, head, springflow) in the southwestern subbasin that includes the El Malpais National Monument, (2) incorporating temporally variable vegetation parameters, (3) spatial downscaling of the hydrometeorological input datasets, (4) incorporating additional spatial variability to hydraulic property parameters on the basis of new data collection, and (5) using environmental tracers to verify and calibrate model parameters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235028","issn":"2328-0328","collaboration":"Prepared in cooperation with the Bureau of Reclamation, Pueblo of Acoma, and Pueblo of Laguna","usgsCitation":"Ritchie, A.B., Chavarria, S.B., Galanter, A.E., Flickinger, A.K., Robertson, A.J., and Sweetkind, D.S., 2023, Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico: U.S. Geological Survey Scientific Investigations Report 2023–5028, 76 p., 1 pl., https://doi.org/10.3133/sir20235028.","productDescription":"Report: x, 76 p.; 1 Plate: 25.37 x 40.38 inches; Data Release","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-111893","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":416632,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5028/coverthb.jpg"},{"id":416635,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235028/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5028 HTML"},{"id":416634,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028.XML","size":"482 KB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5028 XML"},{"id":416638,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028_plate1.pdf","size":"550 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5028 plate 1"},{"id":416633,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028.pdf","size":"5.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5028"},{"id":416637,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRTKTM","text":"USGS data release—GSFLOW, used to run PRMS and MODFLOW-NWT models, to simulate the effects of natural and anthropogenic impacts on water resources in the Rio San Jose Basin and surrounding areas, New Mexico"},{"id":416636,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5028/Images/"},{"id":500886,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114717.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Mexico","otherGeospatial":"Rio San Jose Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.5,\n 36\n ],\n [\n -108.5,\n 36\n ],\n [\n -108.5,\n 34\n ],\n [\n -106.5,\n 34\n ],\n [\n -106.5,\n 36\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Accurate estimates of flood magnitude and frequency are needed to (1) optimize the design and location of infrastructure, including dams, culverts, bridges, industrial buildings, and highways, and (2) inform flood-zoning and flood-insurance studies. The U.S. Geological Survey (USGS), in cooperation with the State of Hawaiʻi Department of Transportation, estimated flood magnitudes for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEP) for unregulated streamgages in Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, using data through water year 2020. Regression equations were developed to estimate flood magnitude and associated frequency at ungaged streams. This study improves upon a previous USGS flood-frequency report (Oki and others, 2010) by including more peak-flow data, implementing new statistical methods in flood-frequency analysis, and using updated techniques to estimate the regional-skewness coefficient (regional skew).
Flood magnitude and frequency at 238 streamgages were estimated—following national guidelines established in Bulletin 17C (England and others, 2019)—by fitting annual peak-flow data to the Log-Pearson Type III distribution using the expected moments algorithm and the PeakFQ flood-frequency software. Potentially influential low outliers in the data were identified and removed using the Multiple Grubbs-Beck Test. An updated regional skew for Hawaiʻi was estimated using the Bayesian weighted least squares/Bayesian generalized least squares method. The updated regional skew employs a constant model for the five islands in the study area and has a value of −0.157 (mean square error of 0.212).
Multiple linear regression techniques were used to develop regression equations that relate basin and climatic characteristics to peak flows at streamgages. The regression equations can be applied to estimate flood magnitude and frequency at ungaged sites. The study area was split into 10 regions—2 regions per island, generally following a leeward/windward division—containing from 9 to 49 streamgages each. The final regression equations for each region were determined with generalized least-squares analysis using the USGS weighted-multiple-linear regression (WREG) program. The standard error of prediction at the 1-percent AEP for the regression equations ranged from 18 to 164 percent; the pseudo coefficient of determination (pseudo-R2) at the 1-percent AEP ranged from 46 to 100 percent. The regression equations performed well for all regions except leeward Molokaʻi and southern Island of Hawaiʻi; for all other regions, the pseudo-R2 values ranged from about 75 to 100 percent. Compared to the regression equations developed by Oki and others (2010), the regression equations in this study generally showed modest improvements, although the magnitude of differences varied for each region.
Peak-flow estimates at the 238 streamgages included in this study are improved by weighting the at-site statistics computed with PeakFQ and the predicted flows based on the regression equations. Results of this study—including the final peak-flow estimates at streamgages and the regional regression equations—are implemented in the USGS StreamStats web application (U.S. Geological Survey, 2023, StreamStats: https://streamstats.usgs.gov/ss/). StreamStats provides a consistent approach for obtaining peak-flow estimates at streamgages and for applying the regional regression equations for estimating peak flows at ungaged locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235014","collaboration":"Prepared in cooperation with the State of Hawaiʻi Department of Transportation","usgsCitation":"Mitchell, J.N., Wagner, D.M., and Veilleux, A.G., 2023, Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020: U.S. Geological Survey Scientific Investigations Report 2023–5014, 66 p. plus 4 appendixes, https://doi.org/10.3133/sir20235014.","productDescription":"Report: vii, ; 8 Tables; 3 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-139812","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":416577,"rank":15,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GGPPV5","text":"USGS data release","description":"USGS data release","linkHelpText":"Data in support of flood-frequency report—Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020"},{"id":416576,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TOQANM","text":"USGS data release","description":"USGS data release","linkHelpText":"Basin characteristic rasters used in the update of Hawaiʻi StreamStats, 2022"},{"id":416575,"rank":13,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N61WJ7","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets for watershed delineation used in the update of Hawaiʻi StreamStats, 2022"},{"id":416566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5014/coverthb.jpg"},{"id":416567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5014/sir20235014.pdf","text":"Report","size":"7.4 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2.1"},{"id":416571,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5014/sir20235014_table2.1.csv","text":"Table 2.1","size":"20 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2023-5014 Table 2.1"},{"id":416570,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5014/sir20235014_table1.3.csv","text":"Table 1.3","size":"3 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2023-5014 Table 1.3"},{"id":416568,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5014/sir20235014_table1.1.csv","text":"Table 1.1","size":"21 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2023-5014 Table 1.1"},{"id":416581,"rank":17,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5014/sir20235014.XML"},{"id":416580,"rank":16,"type":{"id":34,"text":"Image 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States","state":"Hawaii","otherGeospatial":"Kauaʻi, Oʻahu, Molokaʻi, Maui, Hawaiʻi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.92521972722102,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 22.39025206306377\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pacific Islands Science Center
U.S. Geological Survey
1845 Wasp Blvd., B176
Honolulu, HI 96818