Search USGS⌕ Search

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.

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

At least 289 records · Page 16Linked to original sources
USGS2009

Continuous Turbidity Monitoring in the Indian Creek Watershed, Tazewell County, Virginia, 2006-08

Thousands of miles of natural gas pipelines are installed annually in the United States. These pipelines commonly cross streams, rivers, and other water bodies during pipeline construction. A major concern associated with pipelines crossing water bodies is increased sediment loading and the subsequent impact to the ecology of the aquatic system. Several studies have investigated the techniques used to install pipelines across surface-water bodies and their effect on downstream suspended-sediment concentrations. These studies frequently employ the evaluation of suspended-sediment or turbidity data that were collected using discrete sample-collection methods. No studies, however, have evaluated the utility of continuous turbidity monitoring for identifying real-time sediment input and providing a robust dataset for the evaluation of long-term changes in suspended-sediment concentration as it relates to a pipeline crossing. In 2006, the U.S. Geological Survey, in cooperation with East Tennessee Natural Gas and the U.S. Fish and Wildlife Service, began a study to monitor the effects of construction of the Jewell Ridge Lateral natural gas pipeline on turbidity conditions below pipeline crossings of Indian Creek and an unnamed tributary to Indian Creek, in Tazewell County, Virginia. The potential for increased sediment loading to Indian Creek is of major concern for watershed managers because Indian Creek is listed as one of Virginia's Threatened and Endangered Species Waters and contains critical habitat for two freshwater mussel species, purple bean (Villosa perpurpurea) and rough rabbitsfoot (Quadrula cylindrical strigillata). Additionally, Indian Creek contains the last known reproducing population of the tan riffleshell (Epioblasma florentina walkeri). Therefore, the objectives of the U.S. Geological Survey monitoring effort were to (1) develop a continuous turbidity monitoring network that attempted to measure real-time changes in suspended sediment (using turbidity as a surrogate) downstream from the pipeline crossings, and (2) provide continuous turbidity data that enable the development of a real-time turbidity-input warning system and assessment of long-term changes in turbidity conditions. Water-quality conditions were assessed using continuous water-quality monitors deployed upstream and downstream from the pipeline crossings in Indian Creek and the unnamed tributary. These paired upstream and downstream monitors were outfitted with turbidity, pH (for Indian Creek only), specific-conductance, and water-temperature sensors. Water-quality data were collected continuously (every 15 minutes) during three phases of the pipeline construction: pre-construction, during construction, and post-construction. Continuous turbidity data were evaluated at various time steps to determine whether the construction of the pipeline crossings had an effect on downstream suspended-sediment conditions in Indian Creek and the unnamed tributary. These continuous turbidity data were analyzed in real time with the aid of a turbidity-input warning system. A warning occurred when turbidity values downstream from the pipeline were 6 Formazin Nephelometric Units or 15 percent (depending on the observed range) greater than turbidity upstream from the pipeline crossing. Statistical analyses also were performed on monthly and phase-of-construction turbidity data to determine if the pipeline crossing served as a long-term source of sediment. Results of this intensive water-quality monitoring effort indicate that values of turbidity in Indian Creek increased significantly between the upstream and downstream water-quality monitors during the construction of the Jewell Ridge pipeline. The magnitude of the significant turbidity increase, however, was small (less than 2 Formazin Nephelometric Units). Patterns in the continuous turbidity data indicate that the actual pipeline crossing of Indian Creek had little influence of downstream water quality; co

Scientific Investigations Report↗
USGS2016

Selected low-flow frequency statistics for continuous-record streamgages in Georgia, 2013

This report presents the annual and monthly minimum 1- and 7-day average streamflows with the 10-year recurrence interval (1Q10 and 7Q10) for 197 continuous-record streamgages in Georgia. Streamgages used in the study included active and discontinued stations having a minimum of 10 complete climatic years of record as of September 30, 2013. The 1Q10 and 7Q10 flow statistics were computed for 85 streamgages on unregulated streams with minimal diversions upstream, 43 streamgages on regulated streams, and 69 streamgages known, or considered, to be affected by varying degrees of diversions upstream. Descriptive information for each of these streamgages, including the U.S. Geological Survey (USGS) station number, station name, latitude, longitude, county, drainage area, and period of record analyzed also is presented. Kendall’s tau nonparametric test was used to determine the statistical significance of trends in annual and monthly minimum 1-day and 7-day average flows for the 197 streamgages. Significant negative trends in the minimum annual 1-day and 7-day average streamflow were indicated for 77 of the 197 streamgages. Many of these significant negative trends are due to the period of record ending during one of the recent droughts in Georgia, particularly those streamgages with record through the 2013 water year. Long-term unregulated streamgages with 70 or more years of record indicate significant negative trends in the annual minimum 7-day average flow for central and southern Georgia. Watersheds for some of these streamgages have experienced minimal human impact, thus indicating that the significant negative trends observed in flows at the long-term streamgages may be influenced by changing climatological conditions. A Kendall-tau trend analysis of the annual air temperature and precipitation totals for Georgia indicated no significant trends. A comprehensive analysis of causes of the trends in annual and monthly minimum 1-day and 7-day average flows in central and southern Georgia is outside the scope of this study. Further study is needed to determine some of the causes, including both climatological and human impacts, of the significant negative trends in annual minimum 1-day and 7-day average flows in central and southern Georgia. To assess the changes in the annual 1Q10 and 7Q10 statistics over time for long-term continuous streamgages with significant trends in record, the annual 1Q10 and 7Q10 statistics were computed on a decadal accumulated basis for 39 streamgages having 40 or more years of record that indicated a significant trend. Records from most of the streamgages showed a decline in 7Q10 statistics for the decades of 1980–89, 1990–99, and 2000–09 because of the recent droughts in Georgia. Twenty four of the 39 streamgages had complete records from 1980 to 2010, and records from 23 of these gages exhibited a decline in the 7Q10 statistics during this period, ranging from –6.3 to –76.2 percent with a mean of –27.3 percent. No attempts were made during this study to adjust streamflow records or statistical analyses on the basis of trends. The monthly and annual 1Q10 and 7Q10 flow statistics for the entire period of record analyzed in the study are incorporated into the USGS StreamStatsDB, which is a database accessible to users through the recently released USGS StreamStats application for Georgia. StreamStats is a Web-based geographic information system that provides users with access to an assortment of analytical tools that are useful for water-resources planning and management, and for engineering design applications, such as the design of bridges. StreamStats allows users to easily obtain streamflow statistics, basin characteristics, and other information for user-selected streamgages.

Georgia↗
USGS2018

Rapid crop cover mapping for the conterminous United States

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a ‘two model mapping’ approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one ‘crop type model’ to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of ‘other’ crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1 st of September.

Scientific Reports↗
USGS2021

Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17

Evapotranspiration (ET) is the water-vapor flux transported from the surface of the Earth into the atmosphere and is the sum of surface water directly evaporated and subsurface water transpired by plants. ET rates are commonly estimated by using potential or reference ET, which might differ from actual ET rates. Actual evapotranspiration (ETa) rates can be estimated by using the Operational Simplified Surface Energy Balance (SSEBop) model. This report evaluates SSEBop ETa rates at the point and basin scales in Florida and parts of Alabama and Georgia for 2000–17. ETa rates computed by using data from 24 micrometeorological stations in Florida are referred to as mETa rates and were used to quantify biases in the SSEBop ETa rates, stratified by generalized land-use type. Bias was computed as mETa minus SSEBop ETa rates for given generalized land-use types, and bias-correction equations were computed by using least-squares regressions. In addition to mETa rates at station locations, annual average ETa rates calculated from the application of a water-balance method to 55 basins in Florida and parts of Alabama and Georgia were used to assess the accuracy of the annual SSEBop ETa rates at the basin scale. Another independent model used to simulate ETa rates was based on monthly reference ET from the statewide daily reference evapotranspiration (ETo) gridded dataset for Florida computed by using Geostationary Operational Environmental Satellite estimates of solar radiation (GOES ETo). ETa at grid points was computed as monthly GOES ETo multiplied by ratios of monthly mETa to GOES ETo, computed at micrometeorological stations and stratified by each generalized land-use type. The coefficient of determination (R 2 ) between monthly mETa and SSEBop ETa rates for all stations combined improved from 0.37 before bias correction of SSEBop ETa rates to 0.79 after the bias correction stratified by land-use type. For individual land-uses types, R 2 varied from 0.59 for the monthly mETa at a station in the land-use type forest to 0.82 for the monthly mETa at stations in the land-use type shallow-water-table pasture. Root-mean-square error (RMSE) was computed as a function of the difference between SSEBop ETa rates and mETa rates. RMSE of monthly SSEBop ETa rates was 1.27 inches per month before the bias corrections improved to 0.73 inch per month after the bias corrections. RMSE for bias-corrected annual SSEBop ETa rates based on micrometeorological stations with complete years of records ranged from 2.01 inches per year (in/yr) for the land-use type of agriculture to 5.73 in/yr for the land-use type of deep water-table pasture, or 4.96 and 21.21 percent errors relative to annual mETa rates, respectively. Bias-corrected annual SSEBop ETa rates were also compared to annual ETa rates computed by using a water-balance method (wbETa) for 55 basins in Florida. Differences in bias-corrected average annual SSEBop ETa rates and average annual wbETa rates for the 55 basins ranged from −3.67 to 5.29 in/yr (−9.24 to 17.36 percent). RMSE when computed as a function of the differences between annual SSEBop ETa rates and wbETa rates decreased, on average, from 4.13 in/yr for the uncorrected bias SSEBop ETa rates to 1.95 in/yr for the bias-corrected SSEBop rates. The average annual bias-corrected SSEBop ETa rates, from all basins, was 36.46 in/yr or 3.41 percent lower than the average annual wbETa rate of 37.79 inches. Bias in SSEBop ETa rates varies based on time step (monthly versus annual), scale (point, basin, statewide), and land-use type. Applications to hydrologic models should consider bias relative to the inherent error in models. Bias-corrected SSEBop ETa rates could be used as calibration targets in models of hydrologic processes, such as groundwater models. Annual bias in SSEBop ETa introduced to the model calibration is typically below the margin of error associated with typical residuals in model simulations, depending on scale. Surface-water and groundwater-flow models with RMSEs on the order of a few feet could benefit from bias-corrected SSEBop values of ETa.

Alabama, Florida, Georgia↗
USGS2020

Elevation and elevation-change maps of Fountain Creek, southeastern Colorado, 2015–19

The U.S. Geological Survey, in cooperation with Colorado Springs Utilities, has been collecting topographic data at 10 study areas along Fountain Creek, Colorado, annually since 2012. The 10 study areas are located between Colorado Springs and the terminus of Fountain Creek at the Arkansas River in Pueblo. The purpose of this report is to present elevation maps based on topographic surveys collected in 2015 and 2019 and to present maps of elevation change that occurred between 2015 and 2019 at all 10 study areas. Elevation and elevation-change maps were developed in ArcGIS from topographic surveys collected at each study area using real-time kinematic Global Navigation Satellite Systems during the winter months (January through April) of 2015 and 2019. Elevation-change maps were created using statistically defined minimum levels of change detection asso-ciated with the 68-percent confidence limit and the 95-percent confidence limit. Study areas along Fountain Creek underwent a range of geomorphic responses between 2015 and 2019 that often depended on the dominant channel pattern of the study area. The results of this ongoing monitoring effort can be used to assess long-term changes in land-surface elevation and to advance understanding of the geomorphic response to possible alterations in flow conditions on Fountain Creek.

Colorado↗
USGS2022

Comparisons of Coupled Model Intercomparison Project Phase 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) sea-ice projections in polar bear (Ursus maritimus) ecoregions during the 21st century

Climate model projections are commonly used to assess potential impacts of global warming on a breadth of social, economic, and environmental topics. Modeling centers throughout the world coordinate to apply a consistent suite of radiative forcing experiments so that all model outputs can be collectively analyzed and compared. Three generations of model outputs have been produced and made available to the scientific community through the Coupled Model Intercomparison Project (CMIP): CMIP3 disseminated during the mid-2000s, CMIP5 during the early-2010s, and CMIP6 during the late-2010s. Twenty-first century sea-ice projections from CMIP3 and CMIP5 models have been used in Bayesian network assessments of how climate change could impact the future persistence of polar bears ( Ursus maritimus ) throughout their range. In this report, we compare sea-ice projections by CMIP6 models to those of CMIP5 models in each of four polar bear ecoregions over the 21st century. We evaluate differences between the two CMIP generations with respect to other sources of variability that affect uncertainties of the model projections: (1) variability from different models; (2) variability from different greenhouse gas emissions scenarios; and (3) natural (internal) variability in the earth’s climate system. We found that natural variability as well as that attributable to models dominated uncertainties in sea-ice projections in all months and ecoregions during the first half of the 21st century, while emissions scenarios dominated uncertainties during the late 21st century. By comparison, we found only slight differences between the CMIP6 and CMIP5 model projections of sea ice. Applying CMIP6 instead of CMIP5 sea-ice projections to the polar bear Bayesian network model developed in 2016, therefore, would not qualitatively change the population status outcomes published therein.

Open-File Report↗
USGS2019

Wind sheltering impacts on land-atmosphere fluxes over fens

Wetlands and their ability to mitigate climate change motivates restorative and protective action; however, scientific understanding of land-atmosphere interactions is restricted by our limited continuous observations of gaseous fluxes. Many wetlands are small in spatial scale and embedded in forested landscapes. Yet, little is known about how the relative sheltering of forests affects net carbon (C) and energy balance. Here, we analyze coterminous USGS and Ameriflux eddy covariance flux tower observations over three years in two shrub fens in Northern Wisconsin, one more sheltered (US-ALQ) than the other (US-Los). Unsurprisingly, the open site showed higher overall wind speeds. This should have implications for atmospheric fluxes in wetlands as wind-forced processes are essential in promoting gas exchange over water. While both sites had similar half-hourly net ecosystem exchange of CO2 (NEE) during daytime, there were significant differences in nighttime NEE, as well as in net radiation partitioning in early spring and late summer. Sensible heat (H) fluxes were smaller at the sheltered fen except for the months of July-September. In contrast, latent heat (LE) fluxes were higher in every month except July. Additionally, sheltered fen ecosystem respiration had a weaker linear correlation with air temperature (R: 0.08 versus 0.57 for the open fen). Our work suggests that canopy sheltering does not cause significant differences in half-hourly NEE during the day, but rather the largest differences such as lower CO2 emissions occur at nighttime due to higher variance at very low wind speeds. Sheltering also influenced direction of air flow, mean wind speeds in day versus night, energy balance, and sensible and latent heat fluxes. We discuss implications of these findings for wetland restoration.

Wisconsin↗
USGS2024

Streamflow characterization and hydromodification, Indian and Kill Creek Basins, Johnson County, Kansas, 1985–2018

Urban stream restoration requires a quantitative understanding of hydromodification to provide a scientific basis for establishing, prioritizing, and monitoring stream quality improvement goals. A study by the U.S. Geological Survey, in cooperation with the Johnson County Urban stream restoration benefits from a quantitative understanding of hydromodification to provide a scientific basis for establishing, prioritizing, and monitoring stream quality improvement goals. A study by the U.S. Geological Survey, in cooperation with the Johnson County Stormwater Management Program, began in 2017 to assess streamflow conditions at U.S. Geological Survey streamgages along Indian and Kill Creeks in Johnson County, Kansas. These streams represent the most urban (Indian Creek) and least urban (Kill Creek) drainage basins in the county. The assessment used 40 streamflow indicators to characterize streamflow conditions for both streams and quantify the degree of hydromodification for Indian Creek. The 40 streamflow indicators consisted of 35 commonly used indicators for characterizing streamflow, 2 less common seasonality indicators, and 3 other indicators based on duration curves, runoff hydrographs, and streamflow percentile classes. The indicators represented five key components of the natural streamflow regime: magnitude, frequency, duration, timing, and rate of change. As part of the study, indicators were evaluated as to general utility for characterizing streamflow conditions, quantifying hydromodification, and assessing the effectiveness of implemented management practices intended to restore urban streams. Results identifying indicators that serve these purposes could be applied more generally to other streams in Johnson County to assess hydromodification and potential restoration opportunities. Although the same set of streamflow indicators may not apply to other regions, methods and results presented in this report provide guidance, techniques, and perspective for future related or similar studies elsewhere, particularly those designed to quantify hydromodification of urban streams and monitor the effectiveness of restoration efforts. Compared to Kill Creek, which, for the purposes of this study, was considered representative of a least disturbed rural reference condition, Indian Creek hydrology was determined to be substantially modified because of urbanization. Of the 35 streamflow indicators evaluated, 19 indicated a generally consistent and substantial difference between the 2 streams. Hydromodification of Indian Creek was characterized by larger annual mean and monthly mean streamflows (and, thus, larger streamflow volumes), larger low streamflows of shorter duration, larger high streamflows with increased frequency and shorter duration, faster rise and fall rates, and decreased seasonality of high and low streamflows. For the two seasonality indicators, seasonality of high and low streamflows decreased. Duration curves, runoff event hydrographs, and streamflow percentile classes also indicated differences between the two streams for specific ranges of streamflow. Indicators that were useful in identifying generally consistent and substantial differences between the two streams, and therefore demonstrating they collectively or individually may be indicators of hydromodification, included annual median and mean flows; monthly mean flows for February, July, August, September, October, November, and December; all the minimum mean flow indictors (1-day, 3-day, 7-day, 30-day, and 90-day); annual number and mean magnitude of peak flows; some of the flow pulse indicators; and rise and fall rates. Indicators determined to be marginally useful or not useful for identifying consistent and substantial streamflow differences between streams included the flashiness indicators Richards-Baker flashiness index and the fraction of the year the daily mean flow is greater than the annual mean flow, which was not expected. Municipalities are challenged by the need to restore stream quality in urbanized areas where options are limited because of existing development. Understanding hydromodification effects and implications for stream quality can help managers plan urban development that minimizes degradation of stream quality and provides insights for implementing effective management practices. Streamflow indicators identified in this report can be used to guide urban stream restoration. In particular, the most useful indicators could form the basis of numeric criteria for restoration goals aimed at achieving or progressing toward more natural streamflow conditions—and, by extension, more healthy ecosystems—by characterizing flow conditions, quantifying hydromodification, establishing stream-restoration goals, and monitoring progress toward achieving those goals as management practices are implemented.

Kansas↗
USGS2007

Relation between precipitation and the 25th percentile of June and September flows in streams in the Great Lakes, Ohio, and Upper Mississippi River Basins

Regression models were developed for the 25th percentile of June and September flows (first quartile of flow) for 47 streamflow-gaging stations (gaging stations) in the Upper Mississippi, Ohio, and Great Lakes drainage basins. The gaging stations that were selected for this analysis are on unregulated rivers, have at least 40 years of record, and have a nearby weather station with at least 70 years of precipitation record. Regression models were developed for each gaging station relating annual 25th percentile of June and September flows to selected precipitation variables. The explanatory variables are monthly precipitation (April-June, July-September) for each year of record, precipitation for the previous year, and average precipitation for the preceding 5-, 10-, 15-, 20-, 25-, and 30-year periods. Short-term precipitation (April-June or July-September monthly precipitation) variables are the most common significant variables in the regression equations for the 25th percentile of June and September streamflows. May and June monthly precipitation are the most common significant variables among the regression models of the 25th percentile of June flows. August and September monthly precipitation are the most common significant variables in the regression models of the 25th percentile of September streamflow. July precipitation also is a significant explanatory variable in regression models of September streamflow. The 25th-percentile flows in this study also are related to intermediate- and long-term precipitation variables. The intermediate-term precipitation variable (previous-year's precipitation) has a more distinct spatial pattern than the long-term precipitation variable (multiyear running averages of annual precipitation) and is more likely to be significant in the western part than in the eastern part of the study area.

Scientific Investigations Report↗
USGS2011

The water-quality monitoring program for the Baltimore reservoir system, 1981-2007—Description, review and evaluation, and framework integration for enhanced monitoring

The City of Baltimore, Maryland, and parts of five surrounding counties obtain their water from Loch Raven and Liberty Reservoirs. A third reservoir, Prettyboy, is used to resupply Loch Raven Reservoir. Management of the watershed conditions for each reservoir is a shared responsibility by agreement among City, County, and State jurisdictions. The most recent (2005) Baltimore Reservoir Watershed Management Agreement (RWMA) called for continued and improved water-quality monitoring in the reservoirs and selected watershed tributaries. The U.S. Geological Survey (USGS) conducted a retrospective review of the effectiveness of monitoring data obtained and analyzed by the RWMA jurisdictions from 1981 through 2007 to help identify possible improvements in the monitoring program to address RWMA water-quality concerns. Long-term water-quality concerns include eutrophication and sedimentation in the reservoirs, and elevated concentrations of (a) nutrients (nitrogen and phosphorus) being transported from the major tributaries to the reservoirs, (b) iron and manganese released from reservoir bed sediments during periods of deep-water anoxia, (c) mercury in higher trophic order game fish in the reservoirs, and (d) bacteria in selected reservoir watershed tributaries. Emerging concerns include elevated concentrations of sodium, chloride, and disinfection by-products (DBPs) in the drinking water from both supply reservoirs. Climate change and variability also could be emerging concerns, affecting seasonal patterns, annual trends, and drought occurrence, which historically have led to declines in reservoir water quality. Monitoring data increasingly have been used to support the development of water-quality models. The most recent (2006) modeling helped establish an annual sediment Total Maximum Daily Load to Loch Raven Reservoir, and instantaneous and 30-day moving average water-quality endpoints for chlorophyll-a (chl-a) and dissolved oxygen (DO) in Loch Raven and Prettyboy Reservoirs. Modelers cited limitations in data, including too few years with sufficient stormflow data, and (or) a lack of (readily available) data, for selected tributary and reservoir hydrodynamic, water-quality, and biotic conditions. Reservoir monitoring also is too infrequent to adequately address the above water-quality endpoints. Monitoring data also have been effectively used to generally describe trophic states, changes in trophic state or conditions related to trophic state, and in selected cases, trends in water-quality or biotic parameters that reflect RWMA water-quality concerns. Limitations occur in the collection, aggregation, analyses, and (or) archival of monitoring data in relation to most RWMA water-quality concerns. Trophic, including eutrophic, conditions have been broadly described for each reservoir in terms of phytoplankton production, and variations in production related to typical seasonal patterns in the concentration of DO, and hypoxic to anoxic conditions, where the latter have led to elevated concentrations of iron and manganese in reservoir and supply waters. Trend analyses for the period 1981-2004 have shown apparent declines in production (algal counts and possibly chl-a). The low frequency of phytoplankton data collection (monthly or bimonthly, depending on the reservoir), however, limits the development of a model to quantitatively describe and relate temporal variations in phytoplankton production including seasonal succession to changes in trophic states or other reservoir water-quality or biotic conditions. Extensive monitoring for nutrients, which, in excessive amounts, cause eutrophic conditions, has been conducted in the watershed tributaries and reservoirs. Data analyses (1980-90s) have (a) identified seasonal patterns in concentrations, (b) characterized loads from (non)point sources, and (c) shown that different seasonal patterns and trends in nutrient concentrations occur between watershed tributaries and downstream reservoir.

Maryl;Pennsylvania↗
USGS2015

Regression Equations for Monthly and Annual Mean and Selected Percentile Streamflows for Ungaged Rivers in Maine

In an effort to delineate hydrologic conditions in Maine, the U.S. Geological Survey, in cooperation with the Maine Department of Transportation, used streamflow data to develop dependent variables for 130 regression equations for estimating monthly and annual mean and 1, 5, 10, 25, 50, 75, 90, 95, and 99 percentile streamflows for ungaged, unregulated rivers in Maine. Daily streamflow data from 24 rural unregulated basins with drainage areas between 14.9 and 1,419 square miles in Maine and northern New Hampshire were used in the derivation of the equations. Streamflow data collected from October 1, 1982, through September 30, 2012, were used to derive the dependent variables for this study to represent current [2015] hydrologic conditions in Maine and northern New Hampshire. Weighted least squares regression techniques were used to derive the final coefficients and measures of uncertainty for the regression equations. Eight basin characteristics serve as the explanatory variables: drainage area, distance from the coast, mean and maximum basin elevation, mean basin slope, mean basin percentage of hydrologic soil group A, fraction of sand and gravel aquifers, and percentage of open water. The largest average errors of prediction are associated with regression equations for the lowest streamflows derived for months during which the lowest streamflows of the year occur (such as the 5 and 1 monthly percentiles for August and September). The regression equations have been derived on the basis of streamflow and basin characteristics data for unregulated, rural drainage basins without substantial streamflow or drainage modifications (for example, diversions and (or) regulation by dams or reservoirs, tile drainage, irrigation, channelization, and impervious paved surfaces), therefore using the equations for regulated or urbanized basins with substantial streamflow or drainage modifications will yield results of unknown error. Input basin characteristics derived using techniques or datasets other than those documented in this report or using values outside the ranges used to develop these regression equations also will yield results of unknown error.

Maine↗
USGS2016

Estimation of upstream water use with Ohio’s StreamStats application

This report describes the analytical methods and results of a pilot study to enhance the Ohio StreamStats application by adding the ability to obtain water-use information for selected areas in the northeast quadrant of Ohio. Water-use estimates are determined in StreamStats through a simple multistep process. Water-use data used to develop the Ohio StreamStats water-use application were obtained from the Ohio Department of Natural Resources (ODNR) and 2010 countywide estimates of self-supplied domestic water use (hereafter referred to as “domestic water use”) compiled by the U.S. Geological Survey (USGS). With the exception of domestic water uses, monthly time series of reported water uses for 2005–2012 are used to calculate average monthly and average annual withdrawals. Domestic water use is estimated from the USGS 2010 countywide estimates, assuming that water use is distributed uniformly in space and time. Consumptive-use coefficients are used to estimate net withdrawals and facilitate computation of return flows. Temporary water-use registrations for hydraulic fracturing are tabulated separately from the other water uses. Water-use indices are computed by dividing average annual net withdrawals (with and without temporary registrations) by the mean October streamflow estimated with StreamStats. The water-use indices are intended to provide metrics of potential consumptive water use.

Scientific Investigations Report↗
USGS2016

Evaluating external nutrient and suspended-sediment loads to Upper Klamath Lake, Oregon, using surrogate regressions with real-time turbidity and acoustic backscatter data

Executive Summary Suspended-sediment and total phosphorus loads were computed for two sites in the Upper Klamath Basin on the Wood and Williamson Rivers, the two main tributaries to Upper Klamath Lake. High temporal resolution turbidity and acoustic backscatter data were used to develop surrogate regression models to compute instantaneous concentrations and loads on these rivers. Regression models for the Williamson River site showed strong correlations of turbidity with total phosphorus and suspended-sediment concentrations (adjusted coefficients of determination [Adj R 2 ]=0.73 and 0.95, respectively). Regression models for the Wood River site had relatively poor, although statistically significant, relations of turbidity with total phosphorus, and turbidity and acoustic backscatter with suspended sediment concentration, with high prediction uncertainty. Total phosphorus loads for the partial 2014 water year (excluding October and November 2013) were 39 and 28 metric tons for the Williamson and Wood Rivers, respectively. These values are within the low range of phosphorus loads computed for these rivers from prior studies using water-quality data collected by the Klamath Tribes. The 2014 partial year total phosphorus loads on the Williamson and Wood Rivers are assumed to be biased low because of the absence of data from the first 2 months of water year 2014, and the drought conditions that were prevalent during that water year. Therefore, total phosphorus and suspended-sediment loads in this report should be considered as representative of a low-water year for the two study sites. Comparing loads from the Williamson and Wood River monitoring sites for November 2013–September 2014 shows that the Williamson and Sprague Rivers combined, as measured at the Williamson River site, contributed substantially more suspended sediment to Upper Klamath Lake than the Wood River, with 4,360 and 1,450 metric tons measured, respectively. Surrogate techniques have proven useful at the two study sites, particularly in using turbidity to compute suspended-sediment concentrations in the Williamson River. This proof-of-concept effort for computing total phosphorus concentrations using turbidity at the Williamson and Wood River sites also has shown that with additional samples over a wide range of flow regimes, high-temporal-resolution total phosphorus loads can be estimated on a daily, monthly, and annual basis, along with uncertainties for total phosphorus and suspended-sediment concentrations computed using regression models. Sediment-corrected backscatter at the Wood River has potential for estimating suspended-sediment loads from the Wood River Valley as well, with additional analysis of the variable streamflow measured at that site. Suspended-sediment and total phosphorus loads with a high level of temporal resolution will be useful to water managers, restoration practitioners, and scientists in the Upper Klamath Basin working toward the common goal of decreasing nutrient and sediment loads in Upper Klamath Lake.

Oregon↗
USGS2021

A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17

Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse. To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow. The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [ R 2 ]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period. The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature. The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance ( R 2 =0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance ( R 2 =0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent ( R 2 =0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations. The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.

Wyoming↗
USGS2012

Sources and sinks of nitrogen and phosphorus to a deep, oligotrophic lake, Lake Crescent, Olympic National Park, Washington

Lake Crescent, in Olympic National Park in the northwest corner of Washington State is a deep-water lake renowned for its pristine water quality and oligotrophic nature. To examine the major sources and sinks of nutrients (as total nitrogen, total phosphorus, and dissolved nitrate), a study was conducted in the Lake Crescent watershed. The study involved measuring five major inflow streams, the Lyre River as the major outflow, recording weather and climatic data, coring lake bed sediment, and analyzing nutrient chemistry in several relevant media over 14 months. Water samples for total nitrogen, total phosphorous, and dissolved nitrate from the five inflow streams, the outlet Lyre River, and two stations in the lake were collected monthly from May 2006 through May 2007. Periodic samples of shallow water from temporary sampling wells were collected at numerous locations around the lake. Concentrations of nutrients detected in Lake Crescent and tributaries were then applied to the water budget estimates to arrive at monthly and annual loads from various environmental components within the watershed. Other sources, such as leaf litter, pollen, or automobile exhaust were estimated from annual values obtained from various literature sources. This information then was used to construct a nutrient budget for total nitrogen and total phosphorus. The nitrogen budget generally highlights vehicle traffic-diesel trucks in particular-along U.S. Highway 101 as a potential major anthropogenic source of nitrogen compounds in the lake. In contrast, contribution of nitrogen compounds from onsite septic systems appears to be relatively minor related to the other sources identified.

Washington↗
USGS2017

Response of currents and water quality to changes in dam operations in Hoover Reservoir, Columbus, Ohio, August 24–28, 2015

Hoover Reservoir, an important drinking water supply for the City of Columbus, Ohio, has been the source of a series of taste and odor problems in treated drinking water during the past few years. These taste and odor problems were caused by the compounds geosmin and 2-methylisoborneol, which are thought to have been related to cyanobacteria blooms. In an effort to reduce the phosphorus available for cyanobacteria blooms at fall turnover, the City of Columbus began experimenting with the dam’s selective withdrawal system to remove excess phosphorus in the hypolimnion, which is released from bottom sediments during summer anoxic conditions. The U.S. Geological Survey completed two synoptic survey campaigns to assess distributions of water quality and water velocity in the lower part of Hoover Reservoir to provide information on the changes to reservoir dynamics caused by changing dam operations. One campaign (campaign 1) was done while water was being withdrawn from the reservoir through the dam’s middle gate and the other (campaign 2) while water was being withdrawn through the dam’s lower gate. Velocities were measured using an acoustic Doppler current profiler, and water-quality parameters were measured using an autonomous underwater vehicle equipped with water-quality sensors. Along with the water-quality and water-velocity data, meteorological, inflow and outflow discharges, and independent water-quality data were compiled to monitor changes in other parameters that affect reservoir behavior. Monthly nutrient data, collected by the City of Columbus, were also analyzed for trends in concentration during periods of expected stratification. Based on the results of the two campaigns, when compared to withdrawing water through the middle gate, withdrawing water through the lower gate seemed to increase shear-driven mixing across the thermocline, which resulted in an increase in the depth of the epilimnion throughout the lower part of Hoover Reservoir. The observations from this study, if repeatable and driven primarily by changes in gate operations, can inform nutrient management strategies for Hoover Reservoir. Increased mixing across the thermocline may potentially supply nutrients from the hypolimnion to algae in the epilimnion. Although operation of the lower gate has the potential to export nutrients from the hypolimnion (where the concentrations of nutrients have typically been higher during summer months) through two mechanisms (direct withdrawal and mixing into the epilimnion), supply of nutrients to the epilimnion through enhanced mixing could lead to a short-term increase in algal populations. Therefore, further study is recommended to (1) test the repeatability of the results of gate changes on water-quality distributions and circulation patterns in lower Hoover Reservoir, (2) identify the immediate effect of gate changes on nutrient concentrations in the water column, and (3) identify the best management practices to reduce the nutrient storage in the hypolimnion of Hoover Reservoir without increasing the potential for nutrient transport to the highly productive epilimnion.

Ohio↗
USGS2025

Managing water for birds—A tool for the Malheur National Wildlife Refuge, southeastern Oregon

The “Water for Birds Tool” is a spreadsheet-based tool (using Microsoft Excel) designed to help resource managers assess the spatial extent and types of bird habitats in the Malheur National Wildlife Refuge, southeastern Oregon. The tool quantifies the areas of open water, partial water, and water depths on a monthly timescale during the irrigation season (April–July) from 2021 to 2024. This tool combines previously published datasets and models but also incorporates new measurements collected by partners. Results show that the relation between the amount of bird habitat and the extent (partial and open water) of Malheur Lake varies by bird guild. The Donner und Blitzen River supplied all or most of the surface water inflow to Malheur Lake during the analysis years, emphasizing the importance of informed management of the river. Additional gaging of inflows and diversions and better estimates of recharge and irrigated areas can help refine estimates of water use on the refuge.

Oregon↗
USGS2009

Connections Among the Spatial and Temporal Structures in Tidal Currents, Internal Bores, and Surficial Sediment Distributions Over the Shelf off Palos Verdes, California

The topography of the Continental Shelf in the central portion of the Southern California Bight has rapid variations over relatively small spatial scales. The width of the shelf off the Palos Verdes peninsula, just northwest of Los Angeles, California, is only 1 to 3 km. About 7 km southeast of the peninsula, the shelf within San Pedro Bay widens to about 20 km. In 2000, the Los Angeles County Sanitation District began deploying a dense array of moorings in this complex region of the central Southern California Bight to monitor local circulation patterns. Moorings were deployed at 13 sites on the Palos Verdes shelf and within the northwestern portion of San Pedro Bay. At each site, a mooring supported a string of thermistors and an adjacent bottom platform housed an Acoustic Doppler Current Profiler. These instruments collected vertical profiles of current and temperature data continuously for one to two years. The variable bathymetry in the region causes rapid changes in the amplitudes and spatial structures of barotropic tidal currents, internal tidal currents, and in the associated nonlinear baroclinic currents that occur at approximate tidal frequencies. The largest barotropic tidal constituent is M2, the principal semidiurnal tide. The amplitude of this tidal current changes over fairly short along-shelf length scales. Tidal-current amplitudes are largest in the transition region between the two shelves; they increase from about 5 cm/s over the northern San Pedro shelf to nearly 10 cm/s on the southern portion of the Palos Verdes Shelf. Tidal-current amplitudes are then reduced to less than 2 cm/s over the very narrow section of the northern Palos Verdes shelf that lies just 6 km upcoast of the southern sites. Models suggest that the amplitude of the barotropic M2 tidal currents, which propagate toward the northwest primarily as a Kelvin wave, is adjusting to the short topographic length scales in the region. Semidiurnal sea-level oscillations are, as expected, independent of these topographic variations; they have a uniform amplitude and phase structure over the entire region. Because the cross-shelf angle of the seabed over most of the Palos Verdes shelf is 1 to 3 degrees, which is critical for the local generation and/or enhancement of nonlinear characteristics in semidiurnal internal tides, some internal tidal-current events have strong asymmetric current oscillations that are enhanced near the seabed. Near-bottom currents in these events are directed primarily offshore with amplitudes that exceed 30 cm/s. The spatial patterns in these energetic near-bottom currents have fairly short-length scales. They are largest over the inner shelf and in the transition region between the Palos Verdes and San Pedro shelves. This spatial pattern is similar to that found in the barotropic tidal currents. Because these baroclinic currents have an approximate tidal frequency, an asymmetric vertical structure, and a somewhat stable phase, they can produce a non-zero depth-mean flow for periods of a few months. These baroclinic currents can interact with the barotropic tidal current and cause an apparent increase (or decrease) in the estimated barotropic tidal-current amplitude. The apparent amplitude of the barotropic tidal current may change by 30 to 80 percent or more in a current record that is less than three months long. The currents and surficial sediments in this region are in dynamic equilibrium in that the spatial patterns in bottom stresses generated by near-bed currents from surface tides, internal tides, and internal bores partly control the spatial patterns in the local sediments. Coarser sediments are found in the regions with enhanced bottom stresses (that is, over the inner shelf and in the region between the Palos Verdes and San Pedro shelves). Finer sediments are found over the northwestern portion of the Palos Verdes shelf, where near-bottom currents are relatively weak. The nonlinear asymmetries in the i

Scientific Investigations Report↗