SEARCH · Search USGS

Results for “Scientific Monthly”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20Linked to original sources

USGS2006

Hydrologic Characteristics of a Managed Wetland and a Natural Riverine Wetland along the Kankakee River in Northwestern Indiana

Characteristics of ground-water/surface-water interactions were identified at a managed wetland (Hog Marsh) and a natural riverine wetland (LaSalle) located on the north and south sides, respectively, of the Kankakee River in northwestern Indiana. Hog Marsh covers about 390 hectares of the Grand Kankakee Marsh County Park. LaSalle covers about 100 hectares of the LaSalle State Fish and Wildlife Area, and is about 20 kilometers downstream of Hog Marsh. Hydrologic characteristics of the two wetlands were investigated using data from 1997 to 1999 for 22 wells adjacent to the Kankakee River in northwestern Indiana. Surface-water levels at the managed wetland were controlled by a system of channels, levees, and managed flooding. Surface-water levels at the natural riverine wetland were not controlled. Ground-water levels in the unconfined surficial aquifer beneath the two wetlands were analyzed by assessing water-level fluctuations. Fifteen wells at Hog Marsh and seven wells at LaSalle were monitored. The interquartile range in ground-water levels away from the river at Hog Marsh fluctuated less (from 0.4 to 0.7 meters) than all ground-water levels in the same aquifer beneath LaSalle (from 0.9 to 1.0 meters). The difference in the range of water-level fluctuation probably is attributable to the managed flooding of Hog Marsh units, which tends to maintain somewhat uniform water levels in that wetland. Ground-water-flow directions along a vertical section through the unconfined surficial aquifer at the managed wetland were more variable than those at the natural riverine wetland. During winter and spring, when flow in the Kankakee River is high, flow is from the Kankakee River into the adjacent surficial aquifer and towards a 2-meter-wide Brown Ditch on the north side of Hog Marsh. Water levels in Brown Ditch remain lower than those in the Kankakee River during this period. From June to December, when flow in the Kankakee River is moderate to low, a flow divide developed near the center of the managed wetland. Ground-water flow south of the divide is to the Kankakee River; north of the divide, it is toward Brown Ditch. Slight ground-water mounding near the center of the managed wetland is accentuated by water-management practices that intentionally flood that area. Ground-water flow in the surficial aquifer at the natural riverine wetland was not impeded by ditches or managed flooding, and a simple flow-through system from areas south of the Kankakee River to the river was observed. A ground-water flow model was constructed along a representative cross section through the surficial aquifer at the managed wetland and calibrated using data collected at the site. A no-flow boundary was used beneath the Kankakee River, and head-dependent boundaries were used along the north end of the model and at the base of the model. The model simulations indicated that artificial controls on the managed-wetland hydrology create sites of recharge to and discharge from the surficial aquifer that are absent at the natural riverine wetland. The steady-state flow simulation represented flow conditions following a 4-month period of no changes in hydrologic stresses. The simulation results indicated that flow paths originating from flooded areas near the center of the managed wetland are sources of aquifer recharge during the managed-flooding period. Brown Ditch captured almost all of the ground water north of the managed wetland. The simulated water budget along a well transect indicated that 88 percent of inflow to the surficial aquifer beneath the managed wetland was from a distribution channel and from flooding in the management units. These modeling results identify differences in flow patterns between the managed and natural riverine wetlands in addition to those identified by the water-level data. Results of transient simulations indicated that surface water from the Kankakee River penetrated only about 2 to 3 meters into the surficial aquif

Indiana↗

USGS2025

Estimated hydrogeologic, spatial, and temporal distribution of self-supplied domestic groundwater withdrawals for aquifers of the Virginia Coastal Plain

Water use from private-domestic wells accounts for nearly 40 percent of total groundwater withdrawals in the Virginia Coastal Plain Physiographic Province (henceforth called the Virginia Coastal Plain). However, because self-supplied domestic water use generally falls below the Virginia Department of Environmental Quality (VDEQ) reporting and management threshold of 300,000 gallons per month, quantifying these withdrawals is challenging. This report builds upon the foundation of previous U.S. Geological Survey investigations by providing revised techniques to improve estimates of the aquifer source, spatial distribution, and monthly magnitude of these groundwater withdrawals. The aquifer sources of private-domestic wells in the Virginia Coastal Plain were estimated by cross-referencing 8,264 well records from the VDEQ and the Virginia Department of Health to a digital model of the Virginia Coastal Plain hydrogeologic framework. This analysis highlights the regional importance of the Yorktown-Eastover, Potomac, and surficial aquifers. Collectively, these three aquifers account for 80 percent of self-supplied domestic groundwater withdrawals. The population using self-supplied domestic water was estimated using census blocks, well-use ratios, building footprints, and land-use and land-cover data to produce a high-resolution, disaggregated, raster-based dataset. This approach improves upon previous models at the census-block or road-network scale by reducing the low-density spread of the self-supplied domestic population across undeveloped areas and concentrating the population and its corresponding water use in the areas where it is most likely to occur. Results show that an estimated 475,332 people comprise the 2020 self-supplied domestic population of the Virginia Coastal Plain, an increase of 5.7 percent since 2010, and the greatest concentrations of self-supplied domestic population surround large cities. Estimates could be further refined with the addition of current and complete spatial data on public water-system service areas. The quantity of water used by the self-supplied domestic population was estimated by modifying published state per-capita water-use coefficients with the corresponding monthly variability assessed from Virginia Coastal Plain public water-system withdrawal data. This analysis estimates an average increase of 12 percent from June through August and an average decrease of 8 percent from December through March from the baseline annual average of 80 gallons per day per capita, which generally matches similar studies in the eastern United States. The application of these revised methodologies for the estimation of private-domestic wells and the self-supplied domestic population improves understanding of domestic groundwater use in the Virginia Coastal Plain across hydrogeologic, spatial, and temporal scales. These revisions help better inform water-resource managers and decision makers and support higher resolution groundwater modeling. Furthermore, these methods are transferrable to other areas where self-supplied domestic water withdrawals are important to the overall water budget.

Virginia↗

USGS2017

Limiting the effects of earthquakes on gravitational-wave interferometers

Ground-based gravitational wave interferometers such as the Laser Interferometer Gravitational-wave Observatory (LIGO) are susceptible to ground shaking from high-magnitude teleseismic events, which can interrupt their operation in science mode and significantly reduce their duty cycle. It can take several hours for a detector to stabilize enough to return to its nominal state for scientific observations. The down time can be reduced if advance warning of impending shaking is received and the impact is suppressed in the isolation system with the goal of maintaining stable operation even at the expense of increased instrumental noise. Here, we describe an early warning system for modern gravitational-wave observatories. The system relies on near real-time earthquake alerts provided by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). Preliminary low latency hypocenter and magnitude information is generally available in 5 to 20 min of a significant earthquake depending on its magnitude and location. The alerts are used to estimate arrival times and ground velocities at the gravitational-wave detectors. In general, 90% of the predictions for ground-motion amplitude are within a factor of 5 of measured values. The error in both arrival time and ground-motion prediction introduced by using preliminary, rather than final, hypocenter and magnitude information is minimal. By using a machine learning algorithm, we develop a prediction model that calculates the probability that a given earthquake will prevent a detector from taking data. Our initial results indicate that by using detector control configuration changes, we could prevent interruption of operation from 40 to 100 earthquake events in a 6-month time-period.

Classical and Quantum Gravity↗

USGS2005

Streamflow characteristics and trends in New Jersey, water years 1897-2003

Streamflow statistics were computed for 111 continuous-record streamflow-gaging stations with 20 or more years of continuous record and for 500 low-flow partial-record stations, including 66 gaging stations with less than 20 years of continuous record. Daily mean streamflow data from water year 1897 through water year 2001 were used for the computations at the gaging stations. (The water year is the 12-month period, October 1 through September 30, designated by the calendar year in which it ends). The characteristics presented for the long-term continuous-record stations are daily streamflow, harmonic mean flow, flow frequency, daily flow durations, trend analysis, and streamflow variability. Low-flow statistics for gaging stations with less than 20 years of record and for partial-record stations were estimated by correlating base-flow measurements with daily mean flows at long-term (more than 20 years) continuous-record stations. Instantaneous streamflow measurements through water year 2003 were used to estimate low-flow statistics at the partial-record stations. The characteristics presented for partial-record stations are mean annual flow; harmonic mean flow; and annual and winter low-flow frequency. The annual 1-, 7-, and 30-day low- and high-flow data sets were tested for trends. The results of trend tests for high flows indicate relations between upward trends for high flows and stream regulation, and high flows and development in the basin. The relation between development and low-flow trends does not appear to be as strong as for development and high-flow trends. Monthly, seasonal, and annual precipitation data for selected long-term meteorological stations also were tested for trends to analyze the effects of climate. A significant upward trend in precipitation in northern New Jersey, Climate Division 1 was identified. For Climate Division 2, no general increase in average precipitation was observed. Trend test results indicate that high flows at undeveloped, unregulated sites have not been affected by the increase in average precipitation. The ratio of instantaneous peak flow to 3-day mean flow, ratios of flow duration, ratios of high-flow/low-flow frequency, and coefficient of variation were used to define streamflow variability. Streamflow variability was significantly greater among the group of gaging stations located outside the Coastal Plain than among the group of gaging stations located in the Coastal Plain.

Scientific Investigations Report↗

USGS2010

Methods for estimating flow-duration and annual mean-flow statistics for ungaged streams in Oklahoma

Flow statistics can be used to provide decision makers with surface-water information needed for activities such as water-supply permitting, flow regulation, and other water rights issues. Flow statistics could be needed at any location along a stream. Most often, streamflow statistics are needed at ungaged sites, where no flow data are available to compute the statistics. Methods are presented in this report for estimating flow-duration and annual mean-flow statistics for ungaged streams in Oklahoma. Flow statistics included the (1) annual (period of record), (2) seasonal (summer-autumn and winter-spring), and (3) 12 monthly duration statistics, including the 20th, 50th, 80th, 90th, and 95th percentile flow exceedances, and the annual mean-flow (mean of daily flows for the period of record). Flow statistics were calculated from daily streamflow information collected from 235 streamflow-gaging stations throughout Oklahoma and areas in adjacent states. A drainage-area ratio method is the preferred method for estimating flow statistics at an ungaged location that is on a stream near a gage. The method generally is reliable only if the drainage-area ratio of the two sites is between 0.5 and 1.5. Regression equations that relate flow statistics to drainage-basin characteristics were developed for the purpose of estimating selected flow-duration and annual mean-flow statistics for ungaged streams that are not near gaging stations on the same stream. Regression equations were developed from flow statistics and drainage-basin characteristics for 113 unregulated gaging stations. Separate regression equations were developed by using U.S. Geological Survey streamflow-gaging stations in regions with similar drainage-basin characteristics. These equations can increase the accuracy of regression equations used for estimating flow-duration and annual mean-flow statistics at ungaged stream locations in Oklahoma. Streamflow-gaging stations were grouped by selected drainage-basin characteristics by using a k-means cluster analysis. Three regions were identified for Oklahoma on the basis of the clustering of gaging stations and a manual delineation of distinguishable hydrologic and geologic boundaries: Region 1 (western Oklahoma excluding the Oklahoma and Texas Panhandles), Region 2 (north- and south-central Oklahoma), and Region 3 (eastern and central Oklahoma). A total of 228 regression equations (225 flow-duration regressions and three annual mean-flow regressions) were developed using ordinary least-squares and left-censored (Tobit) multiple-regression techniques. These equations can be used to estimate 75 flow-duration statistics and annual mean-flow for ungaged streams in the three regions. Drainage-basin characteristics that were statistically significant independent variables in the regression analyses were (1) contributing drainage area; (2) station elevation; (3) mean drainage-basin elevation; (4) channel slope; (5) percentage of forested canopy; (6) mean drainage-basin hillslope; (7) soil permeability; and (8) mean annual, seasonal, and monthly precipitation. The accuracy of flow-duration regression equations generally decreased from high-flow exceedance (low-exceedance probability) to low-flow exceedance (high-exceedance probability) . This decrease may have happened because a greater uncertainty exists for low-flow estimates and low-flow is largely affected by localized geology that was not quantified by the drainage-basin characteristics selected. The standard errors of estimate of regression equations for Region 1 (western Oklahoma) were substantially larger than those standard errors for other regions, especially for low-flow exceedances. These errors may be a result of greater variability in low flow because of increased irrigation activities in this region. Regression equations may not be reliable for sites where the drainage-basin characteristics are outside the range of values of independent vari

Scientific Investigations Report↗

USGS2005

El Niño and displays of spring-flowering annuals in the Mojave and Sonoran deserts

Although popular and scientific literature frequently assumes a strong connection between El Niño-Southern Oscillation (ENSO) and good displays of spring-flowering annuals in the southwestern United States, such assumptions are based on anecdotal, short-term evidence. The goals of this study were to identify good wildflower years as objectively as possible, to assess the correlation between El Niño and good displays of spring-flowering annuals, and to examine the influence of rainfall amounts on good wildflower years. The terms “good displays” and “good wildflower years” refer to times or places when populations of showy spring-flowering annuals (often called winter annuals) are abundant, robust, and diverse. In the deserts of southeastern California and southern Arizona, good wildflower years occurred about once every 5 to 7 years in the 20th century. The connection between good wildflower years and traditionally defined El Niño episodes was weak, but when El Niño was redefined in a phenologically meaningful way as any calendar year in which the average Southern Oscillation Index (SOI) between July and December was negative, 21 of 27 good wildflower years in the combined deserts were associated with El Niño. Good wildflower years were 3.6 times more likely after redefined El Niño years than after other years. Rain in the months before good wildflower years was at least 30% greater than the long-term average in the Mojave Desert and at least 50% greater in the Sonoran Desert. A diverse flora of spring-flowering annuals occurred in the region during the late Wisconsin and early Holocene, which was a period of wetter, milder winters and cooler summers. Perhaps some species of spring-flowering annuals persist today in the arid southwestern United States only because frequent El Niño conditions recreate the cool, moist conditions of the late Pleistocene.

Journal of the Torrey Botanical Society↗

USGS2025

Estimating daily public supply water use by drinking water service area in New Jersey

This report, prepared in cooperation with the New Jersey Department of Environmental Protection, presents a method for estimating daily public supply water use by drinking water service area systems for New Jersey. The ability to accurately estimate daily public supply water use could help water supply planners in New Jersey better understand and manage the state’s limited water resources and balance the competing needs for freshwater resources. Data sources for this work include daily public supply water-use data from 2016 through 2020 acquired from New Jersey American Water for 15 drinking water service areas and monthly data exported from the New Jersey Department of Environmental Protection’s online water transfer data model database (known as NJWaTr). The two datasets were compared by aggregating the daily data to a monthly timescale. Statistical regression analysis was applied to the daily data, along with climate data, to evaluate what factors are influential in estimating daily fluctuations and trends in daily public supply water use. Fifteen regression equations were developed, one for each of the 15 drinking water service area systems for which daily data were acquired. Regression equations for systems that had seasonal patterns performed better than equations for non-seasonal systems. For the test year (2020), the average adjusted coefficient of determination for the linear regression with autoregressive errors model among systems with seasonality was 0.78; the average adjusted coefficient of determination for the linear regression with autoregressive errors model among systems with little or no seasonality was 0.25. The effects of anomalous data in the regression analysis were examined by comparing adjusted coefficient of determination values when the atypical data points were removed versus when they were retained in the analysis. Overall, including the anomalous data did not have a large effect on the results, and thus the data were retained for this study. In addition to developing regression equations, all 589 unique drinking water service area systems in New Jersey were characterized based on socio-economic data and monthly water-use data from NJWaTr. Systems that are located near the New Jersey coast, serve populations larger than 1,970 people, or serve areas that have median property values over $256,250 tended to demonstrate seasonal water-use behaviors. Systems that have mostly urban residential land use tended to show little to no seasonal water-use behaviors. Finally, a method was developed to disaggregate monthly data to a daily timescale and was tested against systems for which daily data were not available. Two regression equation forms were developed to be applied to systems beyond the 15 systems from which the original equations were developed; one equation was developed for use when all drinking water service area systems showed little to no seasonality, and the other equation was developed for use when systems displayed seasonal behavior. To the extent possible, uncertainty and possible sources of error were identified and examined in relation to the regression model equations developed. Additional daily data from these 15 systems (over different years) and daily data from different systems could be used to further evaluate the results of the disaggregation through a comprehensive assessment of error. Further adjustments to the regression equations could be made, ultimately enhancing their accuracy.

New Jersey↗

USGS2006

Selected Streamflow Statistics and Regression Equations for Predicting Statistics at Stream Locations in Monroe County, Pennsylvania

A suite of 28 streamflow statistics, ranging from extreme low to high flows, was computed for 17 continuous-record streamflow-gaging stations and predicted for 20 partial-record stations in Monroe County and contiguous counties in north-eastern Pennsylvania. The predicted statistics for the partial-record stations were based on regression analyses relating inter-mittent flow measurements made at the partial-record stations indexed to concurrent daily mean flows at continuous-record stations during base-flow conditions. The same statistics also were predicted for 134 ungaged stream locations in Monroe County on the basis of regression analyses relating the statistics to GIS-determined basin characteristics for the continuous-record station drainage areas. The prediction methodology for developing the regression equations used to estimate statistics was developed for estimating low-flow frequencies. This study and a companion study found that the methodology also has application potential for predicting intermediate- and high-flow statistics. The statistics included mean monthly flows, mean annual flow, 7-day low flows for three recurrence intervals, nine flow durations, mean annual base flow, and annual mean base flows for two recurrence intervals. Low standard errors of prediction and high coefficients of determination (R2) indicated good results in using the regression equations to predict the statistics. Regression equations for the larger flow statistics tended to have lower standard errors of prediction and higher coefficients of determination (R2) than equations for the smaller flow statistics. The report discusses the methodologies used in determining the statistics and the limitations of the statistics and the equations used to predict the statistics. Caution is indicated in using the predicted statistics for small drainage area situations. Study results constitute input needed by water-resource managers in Monroe County for planning purposes and evaluation of water-resources availability.

Scientific Investigations Report↗

USGS2012

Simulation of groundwater and surface-water resources and evaluation of water-management alternatives for the Chamokane Creek basin, Stevens County, Washington

A three-dimensional, transient numerical model of groundwater and surface-water flow was constructed for Chamokane Creek basin to better understand the groundwater-flow system and its relation to surface-water resources. The model described in this report can be used as a tool by water-management agencies and other stakeholders to quantitatively evaluate the effects of potential increases in groundwater pumping on groundwater and surface-water resources in the basin. The Chamokane Creek model was constructed using the U.S. Geological Survey (USGS) integrated model, GSFLOW. GSFLOW was developed to simulate coupled groundwater and surface-water resources. The model uses 1,000-foot grid cells that subdivide the model domain by 102 rows and 106 columns. Six hydrogeologic units in the model are represented using eight model layers. Daily precipitation and temperature were spatially distributed and subsequent groundwater recharge was computed within GSFLOW. Streamflows in Chamokane Creek and its major tributaries are simulated in the model by routing streamflow within a stream network that is coupled to the groundwater-flow system. Groundwater pumpage and surface-water diversions and returns specified in the model were derived from monthly and annual pumpage values previously estimated from another component of this study and new data reported by study partners. The model simulation period is water years 1980-2010 (October 1, 1979, to September 30, 2010), but the model was calibrated to the transient conditions for water years 1999-2010 (October 1, 1998, to September 30, 2010). Calibration was completed by using traditional trial-and-error methods and automated parameter-estimation techniques. The model adequately reproduces the measured time-series groundwater levels and daily streamflows. At well observation points, the mean difference between simulated and measured hydraulic heads is 7 feet with a root-mean-square error divided by the total difference in water levels of 4.7 percent. Simulated streamflow was compared to measured streamflow at the USGS streamflow-gaging station-Chamokane Creek below Falls, near Long Lake (12433200). Annual differences between measured and simulated streamflow for the site ranged from -63 to 22 percent. Calibrated model output includes a 31-year estimate of monthly water budget components for the hydrologic system. Five model applications (scenarios) were completed to obtain a better understanding of the relation between groundwater pumping and surface-water resources. The calibrated transient model was used to evaluate: (1) the connection between the upper- and middle-basin groundwater systems, (2) the effect of surface-water and groundwater uses in the middle basin, (3) the cumulative impacts of claims registry use and permit-exempt wells on Chamokane Creek streamflow, (4) the frequency of regulation due to impacted streamflow, and (5) the levels of domestic and stockwater use that can be regulated. The simulation results indicated that streamflow is affected by existing groundwater pumping in the upper and middle basins. Simulated water-management scenarios show streamflow increased relative to historical conditions as groundwater and surface-water withdrawals decreased.

Washington↗

USGS2016

Stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural streamflow

The Souris River Basin is a 61,000-square-kilometer basin in the Provinces of Saskatchewan and Manitoba and the State of North Dakota. In May and June of 2011, record-setting rains were seen in the headwater areas of the basin. Emergency spillways of major reservoirs were discharging at full or nearly full capacity, and extensive flooding was seen in numerous downstream communities. To determine the probability of future extreme floods and droughts, the U.S. Geological Survey, in cooperation with the North Dakota State Water Commission, developed a stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural (unregulated) streamflow. Simulations from the model can be used in future studies to simulate regulated streamflow, design levees, and other structures; and to complete economic cost/benefit analyses. Long-term climatic variability was analyzed using tree-ring chronologies to hindcast precipitation to the early 1700s and compare recent wet and dry conditions to earlier extreme conditions. The extended precipitation record was consistent with findings from the Devils Lake and Red River of the North Basins (southeast of the Souris River Basin), supporting the idea that regional climatic patterns for many centuries have consisted of alternating wet and dry climate states. A stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration for the Souris River Basin was developed using recorded meteorological data and extended precipitation records provided through tree-ring analysis. A significant climate transition was seen around1970, with 1912–69 representing a dry climate state and 1970–2011 representing a wet climate state. Although there were some distinct subpatterns within the basin, the predominant differences between the two states were higher spring through early fall precipitation and higher spring potential evapotranspiration for the wet compared to the dry state. A water-balance model was developed for simulating monthly natural (unregulated) mean streamflow based on precipitation, temperature, and potential evapotranspiration at select streamflow-gaging stations. The model was calibrated using streamflow data from the U.S. Geological Survey and Environment Canada, along with natural (unregulated) streamflow data from the U.S. Army Corps of Engineers. Correlation coefficients between simulated and natural (unregulated) flows generally were high (greater than 0.8), and the seasonal means and standard deviations of the simulated flows closely matched the means and standard deviations of the natural (unregulated) flows. After calibrating the model for a monthly time step, monthly streamflow for each subbasin was disaggregated into three values per month, or an approximately 10-day time step, and a separate routing model was developed for simulating 10-day streamflow for downstream gages. The stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration was combined with the water-balance model to simulate potential future sequences of 10-day mean streamflow for each of the streamflow-gaging station locations. Flood risk, as determined by equilibrium flow-frequency distributions for the dry (1912–69) and wet (1970–2011) climate states, was considerably higher for the wet state compared to the dry state. Future flood risk will remain high until the wet climate state ends, and for several years after that, because there may be a long lag-time between the return of drier conditions and the onset of a lower soil-moisture storage equilibrium.

Manitoba, North Dakota, Saskatchewan↗

USGS2020

Models with environmental drivers offer a plausible mechanism for the rapid spread of infectious disease outbreaks in marine organisms

The first signs of sea star wasting disease (SSWD) epidemic occurred in just few months in 2013 along the entire North American Pacific coast. Disease dynamics did not manifest as the typical travelling wave of reaction-diffusion epidemiological model, suggesting that other environmental factors might have played some role. To help explore how external factors might trigger disease, we built a coupled oceanographic-epidemiological model and contrasted three hypotheses on the influence of temperature on disease transmission and pathogenicity. Models that linked mortality to sea surface temperature gave patterns more consistent with observed data on sea star wasting disease, which suggests that environmental stress could explain why some marine diseases seem to spread so fast and have region-wide impacts on host populations.

Baja California, British Columbia, California, Ore↗

USGS2008

Estimated use of water in the Upper Duck River watershed, central Tennessee, and water-demand projections through 2030

Future municipal water demand was estimated for the Bedford, Coffee, Marshall, and Maury-southern Williamson water-service areas in the upper Duck River watershed in central Tennessee through 2030. The Duck River, a primary source of municipal water, provided a total of 24.3 million gallons per day (Mgal/d) or 92 percent of the total water use in the study area during 2000. Municipal water use increased 46 percent from 1981 to 2000 (from 18.0 to 26.3 Mgal/d). Water demand for municipal use is expected to continue to increase through 2030 because of the recent intensive and anticipated growth in the residential and commercial sectors. Constant-rate models were used to estimate future municipal water demand. Data on residential and nonresidential billing accounts and estimates of public use and losses were used to calibrate the models. Two watershed scenarios for each water-supply system that depends on the Duck River for supply were simulated. Scenario 1 considered monthly water demand during typical weather conditions as represented by monthly per account use during 2003 and a rate of growth in customer accounts from 1999 to 2003. Results showed that total municipal water use could increase about 104 percent to 51 Mgal/d by 2030, residential water use could increase about 140 percent to 24 Mgal/d, nonresidential water use could increase about 110 percent to 17 Mgal/d, and public use and losses could increase about 83 percent to 11 Mgal/d. Scenario 2 considered monthly water demand during drought conditions as represented by monthly per account use during 2000 and recent growth in customer accounts from 1999 to 2003 or, for selected water-supply systems, an increasing rate of growth. Results showed that total municipal water use could increase about 120 percent to 55 Mgal/d, residential water use could increase about 160 percent to 26 Mgal/d, nonresidential water use could increase about 122 percent to 18 Mgal/d, and public use and losses could double and increase to 12 Mgal/d. For both scenarios the model assumed that the Duck River would supply all future surface-water needs in the study area, that ground-water resources would be sufficient to meet growing demands of the water-supply systems that depend on ground water, and that the amount of surface water sold to water-supply systems primarily dependent on ground water would remain the same through 2030.

Tennessee↗

USGS2008

Portable chamber measurements of evapotranspiration at the Amargosa Desert Research Site near Beatty, Nye County, Nevada, 2003-06

Portable chamber measurements of evapotranspiration (ET) were made at the U.S. Geological Survey's Amargosa Desert Research Site in southern Nevada to help quantify component- and landscape-scale contributions to ET in an arid environment. Evapotranspiration data were collected approximately every 3 months from 2003 to 2006. Chamber measurements of ET were partitioned into bare-soil evaporation and mixed-species transpiration components. The component-scale ET fluxes from native shrubs typically surpassed those from bare soil by as much as a factor of four. Component-scale ET fluxes were extrapolated to landscape-scale ET using a one-layer, multi-component canopy model. Landscape-scale ET fluxes predominantly were controlled by bare-soil evaporation. Bare soil covered 94 percent of the landscape on average and contributed about 70 percent of the landscape-scale vapor flux. Creosote bush, an evergreen shrub, accounted for about 90 percent of transpiration on average due to its dominance across the landscape (80 percent of the 6 percent shrub cover) and evergreen character.

Nevada↗

USGS2018

Tritium deposition in precipitation in the United States, 1953–2012

Tritium is a radioactive isotope of hydrogen (half-life is equal to 12.32 years). Since it is part of the water molecule, tritium can be used to track and date groundwater and surface water when the history of tritium in precipitation and recharge is known. To facilitate that effort, tritium concentrations in precipitation were reconstructed from measurements and correlations for 10 precipitation stations in the continental United States. Using these data, and other data sets and correlations, estimates of tritium concentrations in precipitation from 1953 through 2012 and total tritium deposition from 1953 through 1983 were derived for 2 degrees latitude by 5 degrees longitude quadrangles of the continental United States. For August 1953 through December 1987 correlations were derived from the data of the Ottawa, Canada, station; for estimates after 1987, the Vienna, Austria, station was used. For quadrangles where no precipitation station records are available from measurements or correlations, concentrations were interpolated. The International Atomic Energy Agency has proposed that a correlation with the Vienna, Austria, station, which has been in operation since 1960, be used instead of the Ottawa, Canada, station for the period after 1987. Linear correlations calculated for all stations with the Vienna data are reported. Correlations also have been calculated for each latitude-longitude quadrangle using the estimated concentrations and the measured Vienna data for 1960–87. Because the Vienna, Austria, station is on a different continent, and not subject to the same seasonal weather patterns that affect North American stations, a correlation was obtained for each month individually. This reflects the fact that the North American stations were strongly affected by the input of low-tritium moisture from the south during the summer. The correlated station values were then used with measured Vienna data to obtain estimates of tritium concentrations in precipitation for the period 1988–2012 at each precipitation station and latitude-longitude quadrangles. This approach has two major advantages: (1) it blends the Ottawa correlation with the Vienna correlation to cover the period 1953 to present, and (2) it yields correlation coefficients for each quadrangle that can be used in future years when more data become available from the Vienna, Austria, station. The data, the estimated tritium concentrations derived from the correlations, and the correlation coefficients are provided as spreadsheets and associated comma delimited files in a data release that accompanies this report.

Scientific Investigations Report↗

USGS2019

Flood-frequency estimates for Ohio streamgages based on data through water year 2015 and techniques for estimating flood-frequency characteristics of rural, unregulated Ohio streams

Estimates of the magnitudes of annual peak streamflows with annual exceedance probabilities of 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, and 0.002 (equivalent to recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively) were computed for 391 streamgages in Ohio and adjacent states based on data collected through the 2015 water year. The flood-frequency estimates were computed following guidance outlined in Bulletin 17C, developed by the Advisory Committee on Water Information. The Bulletin 17C guidelines retain the basic statistical framework of the superseded Bulletin 17B guidelines; however, the Bulletin 17C guidelines add several enhancements including an improved method of moments approach for fitting the log-Pearson Type III (LPIII) distribution to the flood peaks (called the expected moments algorithm), a generalization of the Grubbs Beck low-outlier test (called the Multiple Grubbs Beck test) that permits identification of multiple potentially influential low floods, and new methods for estimating regional skew and uncertainty. Equations for estimating flood-frequency characteristics at ungaged sites on rural, unregulated streams in Ohio were developed with a two-step process involving ordinary least-squares and generalized least-squares regression techniques. Data from 333 streamgages with 10 or more years of unregulated record were screened for redundancy and a regression dataset was selected that was composed of flood-frequency and basin-characteristic data for 275 streamgages in Ohio and adjacent states. Two sets of equations were developed—one set, referred to as the “simple model,” uses regression region and drainage area as regressor variables, and a second set, referred to as the “full model,” uses regression region, drainage area, main-channel slope, and the percentage of the watershed covered by water and wetlands as regressor variables. The average standard errors of prediction ranged from about 40.5 to 46.5 percent for the simple-model equations and from about 37.2 to 40.3 percent for the full-model equations. For sites meeting the rural, unregulated criteria, flood-frequency estimates determined by means of LPIII analyses are reported along with weighted flood-frequency estimates, computed as a function of the LPIII estimates and the regression estimates. For sites with homogenous periods of regulation, flood-frequency estimates determined by means of LPIII analyses are reported. Ninety-five percent confidence limits are reported for all estimates. Values of regressor variables were determined from digital spatial datasets by means of a geographic information system (GIS). The GIS datasets and the new full-model equations have been incorporated into Ohio’s StreamStats application, a web-based, GIS-backed system designed to facilitate the estimation of streamflow statistics at ungaged locations on streams. Seasonal patterns in peak flows were assessed for 295 streamgages in Ohio. Annual peak flows occurred most frequently between January and April, with March having the highest frequency of occurrence. The month with the fewest number of annual peaks was October. Peak-of-record flows occurred most frequently in March, followed by January (months in which two of Ohio’s most severe widespread floods in recent history occurred). None of the peak-of-record flows occurred in October and only two occurred in November. Temporal trend in annual peak flows were assessed for 133 streamgages on unregulated streams in Ohio with 30 or more years of systematic record. Trends were assessed by computing the rank correlation (as measured with the two-sided Kendall’s tau statistic) between time and annual peak flows. Weak but statistically significant trends were indicated at 15 of the 133 streamgages. Of the 15 streamgages with significant trend in annual peak flows, 12 had an upward trend (positive tau) and 3 had a downward trend (negative tau). All 12 streamgages with positive tau values were at latitudes north of 40°33', and streamgages with negative tau values were at latitudes south of 40°33'.

Ohio↗

USGS2025

Peak streamflow trends in North Dakota and their relation to changes in climate, water years 1921–2020

Standardized guidelines for completing flood-flow frequency analyses are presented in a U.S. Geological Survey Techniques and Methods report known as Bulletin 17C, https://doi.org/10.3133/tm4B5 . In recent decades (since about 2000), a better understanding of long-term climatic persistence (periods of clustered floods or droughts, or wet or dry periods) and concerns about potential climate change and land-use change have caused a reexamination of the stationarity assumptions underlying methods in Bulletin 17C. Bulletin 17C does not offer guidance on incorporating nonstationarities and further identifies a need for flood-frequency studies that incorporate changing climate or basin characteristics. As part of that reexamination, a study of annual peak streamflow (peak flow) has begun in the Midwest. This chapter of the study summarizes how hydroclimatic variability affects peak flows in North Dakota. In this analysis of peak flow, daily streamflow, and climate metrics, four periods were selected: (1) a 100-year period, 1921–2020; (2) a 75-year period, 1946–2020; (3) a 50-year period, 1971–2020; and (4) a 30-year period, 1991–2020. Output from a monthly water-balance model was used for the climate data. Statistical analysis of peak flow consisted of evaluations of autocorrelation, trends, and change points and was augmented with analyses of seasonality and daily streamflow. The long-term pattern of decreasing peak flow in the west and increasing peak flow in the east is a pattern of opposing signals on either side of the 100th meridian. Analyses indicate that a key factor in changing hydroclimatology is the increase in fall precipitation. The trends in soil moisture closely match the trends in annual precipitation. Nonstationary flood-frequency analysis necessitates detailed exploratory data analysis and additional data and information about climate, land use, and other factors. This study provides extensive exploratory analysis for peak flow, daily streamflow, and climate data for North Dakota, setting the stage for informed nonstationary flood-frequency analysis.

North Dakota↗

USGS2012

Simulation of streamflow and the effects of brush management on water yields in the upper Guadalupe River watershed, south-central Texas, 1995-2010

The U.S. Geological Survey, in cooperation with the Texas State Soil and Water Conservation Board and the Upper Guadalupe River Authority, developed and calibrated a Soil and Water Assessment Tool watershed model of the upper Guadalupe River watershed in south-central Texas to simulate streamflow and the effects of brush management on water yields in the watershed and to Canyon Lake for 1995–2010. Model simulations were done to quantify the possible change in water yield of individual subbasins in the upper Guadalupe River watershed as a result of the replacement of ashe juniper ( Juniperus ashei ) with grasslands. The simulation results will serve as a tool for resource managers to guide their brush-management efforts. Model hydrology was calibrated with streamflow data collected at the U.S. Geological Survey streamflow-gaging station 08167500 Guadalupe River near Spring Branch, Tex., for 1995–2010. Simulated monthly streamflow showed very good agreement with measured monthly streamflow: a percent bias of -5, a coefficient of determination of 0.91, and a Nash–Sutcliffe coefficient of model efficiency of 0.85. Modified land-cover input datasets were generated for the model in order to simulate the replacement of ashe juniper with grasslands in 23 brush-management subbasins in the watershed. Each of the 23 simulations showed an increase in simulated water yields in the targeted subbasins and to Canyon Lake. The simulated increases in average annual water yields in the subbasins ranged from 6,370 to 119,000 gallons per acre of ashe juniper replaced with grasslands with an average of 38,900 gallons. The simulated increases in average annual water yields to Canyon Lake from upstream subbasins ranged from 6,640 to 72,700 gallons per acre of ashe juniper replaced with grasslands with an average of 34,700 gallons.

Texas↗

USGS2022

Using microbial source tracking to identify fecal contamination sources in South Oyster Bay on Long Island, New York

The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering South Oyster Bay, a shallow embayment on the southern shore of Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the bay and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment. Analysis revealed that the most substantial source of fecal contamination to South Oyster Bay was stormwater, particularly during the summer months. The highest frequency of fecal coliform detections in source sites were under wet summer conditions, and the highest fecal coliform concentrations were under wet summer conditions at the Cedar Creek near Bay Place and Unqua Lake Culvert sites (more than 16,000 most probable number per 100 milliliters each). The human-associated Bacteroides marker was the most frequently detected microbial source tracking marker in South Oyster Bay (50 percent positive detections). The human marker was detected at least twice in all surface water source and receptor sites, except for the Massapequa Lake East Culvert source site that did not have any positive human marker detections. Canine contamination was prolific at source sites but was associated with low fecal coliform concentrations in the winter months. All detections of the canine-associated Bacteroides marker were in samples collected during the winter season and were associated with fecal coliform concentrations below the reporting limit, indicating that birds are not a persistent source of fecal coliform to South Oyster Bay. The absence of fecal coliform and human markers in groundwater samples collected throughout the larger study area indicates that water from cesspools or septic tanks do not contribute fecal coliform to the bay. Further, microbial source tracking markers were not detected in the sandy sediment collected at Zachs Bay. Based a classification scheme developed to convey the degree of fecal contamination to stakeholders and resource managers, the Cedar Creek near Bay Place and Unqua Lake Culvert sites were identified as locations that contribute substantial fecal contamination to South Oyster Bay.

New York↗