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At least 271 records · Page 15Linked to original sources

USGS1966

Preliminary flood-frequency relations for small streams in Kansas

Preliminary flood-frequency relations have been defined for small streams in Kansas for floods having recurrence intervals not greater than 10 years. The defined relations will be useful for the design of culverts and other hydraulic structures. The relations are expressed in terms of basin characteristics. Peakflow records at 95 sites in Kansas for an 8-year period provided the basic data. The records were analyzed with respect to 20 basin characteristics by multiple-regression techniques. The resulting formulas relate flood magnitude and frequency to size of contributing drainage area, an index of stream-bed slope, and the average number of days per year when rainfall exceeded 1.0 inch. The other 17 factors had no statistical significance. To illustrate a typical application of the flood-frequency relation, a step-bystep method is presented for computing a frequency curve for Rock Creek near Meriden, Kans. The frequency curve shows that a peak discharge of 3,620 cfs (cubic feet per second) can be expected once every 10 years on the average, and that the 67 percent confidence interval ranges from 1,820 cfs to 7,230 cfs. The large range results from the fact that only 8 years of record have been collected and emphasizes the need for collecting records for a longer period.

Kansas↗

USGS2013

The use of process models to inform and improve statistical models of nitrate occurrence, Great Miami River Basin, southwestern Ohio

Statistical models of nitrate occurrence in the glacial aquifer system of the northern United States, developed by the U.S. Geological Survey, use observed relations between nitrate concentrations and sets of explanatory variables—representing well-construction, environmental, and source characteristics— to predict the probability that nitrate, as nitrogen, will exceed a threshold concentration. However, the models do not explicitly account for the processes that control the transport of nitrogen from surface sources to a pumped well and use area-weighted mean spatial variables computed from within a circular buffer around the well as a simplified source-area conceptualization. The use of models that explicitly represent physical-transport processes can inform and, potentially, improve these statistical models. Specifically, groundwater-flow models simulate advective transport—predominant in many surficial aquifers— and can contribute to the refinement of the statistical models by (1) providing for improved, physically based representations of a source area to a well, and (2) allowing for more detailed estimates of environmental variables. A source area to a well, known as a contributing recharge area, represents the area at the water table that contributes recharge to a pumped well; a well pumped at a volumetric rate equal to the amount of recharge through a circular buffer will result in a contributing recharge area that is the same size as the buffer but has a shape that is a function of the hydrologic setting. These volume-equivalent contributing recharge areas will approximate circular buffers in areas of relatively flat hydraulic gradients, such as near groundwater divides, but in areas with steep hydraulic gradients will be elongated in the upgradient direction and agree less with the corresponding circular buffers. The degree to which process-model-estimated contributing recharge areas, which simulate advective transport and therefore account for local hydrologic settings, would inform and improve the development of statistical models can be implicitly estimated by evaluating the differences between explanatory variables estimated from the contributing recharge areas and the circular buffers used to develop existing statistical models. The larger the difference in estimated variables, the more likely that statistical models would be changed, and presumably improved, if explanatory variables estimated from contributing recharge areas were used in model development. Comparing model predictions from the two sets of estimated variables would further quantify—albeit implicitly—how an improved, physically based estimate of explanatory variables would be reflected in model predictions. Differences between the two sets of estimated explanatory variables and resultant model predictions vary spatially; greater differences are associated with areas of steep hydraulic gradients. A direct comparison, however, would require the development of a separate set of statistical models using explanatory variables from contributing recharge areas. Area-weighted means of three environmental variables—silt content, alfisol content, and depth to water from the U.S. Department of Agriculture State Soil Geographic (STATSGO) data—and one nitrogen-source variable (fertilizer-application rate from county data mapped to Enhanced National Land Cover Data 1992 (NLCDe 92) agricultural land use) can vary substantially between circular buffers and volume-equivalent contributing recharge areas and among contributing recharge areas for different sets of well variables. The differences in estimated explanatory variables are a function of the same factors affecting the contributing recharge areas as well as the spatial resolution and local distribution of the underlying spatial data. As a result, differences in estimated variables between circular buffers and contributing recharge areas are complex and site specific as evidenced by differences in estimated variables for circular buffers and contributing recharge areas of existing public-supply and network wells in the Great Miami River Basin. Large differences in areaweighted mean environmental variables are observed at the basin scale, determined by using the network of uniformly spaced hypothetical wells; the differences have a spatial pattern that generally is similar to spatial patterns in the underlying STATSGO data. Generally, the largest differences were observed for area-weighted nitrogen-application rate from county and national land-use data; the basin-scale differences ranged from -1,600 (indicating a larger value from within the volume-equivalent contributing recharge area) to 1,900 kilograms per year (kg/yr); the range in the underlying spatial data was from 0 to 2,200 kg/yr. Silt content, alfisol content, and nitrogen-application rate are defined by the underlying spatial data and are external to the groundwater system; however, depth to water is an environmental variable that can be estimated in more detail and, presumably, in a more physically based manner using a groundwater-flow model than using the spatial data. Model-calculated depths to water within circular buffers in the Great Miami River Basin differed substantially from values derived from the spatial data and had a much larger range. Differences in estimates of area-weighted spatial variables result in corresponding differences in predictions of nitrate occurrence in the aquifer. In addition to the factors affecting contributing recharge areas and estimated explanatory variables, differences in predictions also are a function of the specific set of explanatory variables used and the fitted slope coefficients in a given model. For models that predicted the probability of exceeding 1 and 4 milligrams per liter as nitrogen (mg/L as N), predicted probabilities using variables estimated from circular buffers and contributing recharge areas generally were correlated but differed significantly at the local and basin scale. The scale and distribution of prediction differences can be explained by the underlying differences in the estimated variables and the relative weight of the variables in the statistical models. Differences in predictions of exceeding 1 mg/L as N, which only includes environmental variables, generally correlated with the underlying differences in STATSGO data, whereas differences in exceeding 4 mg/L as N were more spatially extensive because that model included environmental and nitrogen-source variables. Using depths to water from within circular buffers derived from the spatial data and depths to water within the circular buffers calculated from the groundwater-flow model, restricted to the same range, resulted in large differences in predicted probabilities. The differences in estimated explanatory variables between contributing recharge areas and circular buffers indicate incorporation of physically based contributing recharge area likely would result in a different set of explanatory variables and an improved set of statistical models. The use of a groundwater-flow model to improve representations of source areas or to provide more-detailed estimates of specific explanatory variables includes a number of limitations and technical considerations. An assumption in these analyses is that (1) there is a state of mass balance between recharge and pumping, and (2) transport to a pumped well is under a steady state flow field. Comparison of volumeequivalent contributing recharge areas under steady-state and transient transport conditions at a location in the southeastern part of the basin shows the steady-state contributing recharge area is a reasonable approximation of the transient contributing recharge area after between 10 and 20 years of pumping. The first assumption is a more important consideration for this analysis. A gradient effect refers to a condition where simulated pumping from a well is less than recharge through the corresponding contributing recharge area. This generally takes place in areas with steep hydraulic gradients, such as near discharge locations, and can be mitigated using a finer model discretization. A boundary effect refers to a condition where recharge through the contributing recharge area is less than pumping. This indicates other sources of water to the simulated well and could reflect a real hydrologic process. In the Great Miami River Basin, large gradient and boundary effects—defined as the balance between pumping and recharge being less than half—occurred in 5 and 14 percent of the basin, respectively. The agreement between circular buffers and volume-equivalent contributing recharge areas, differences in estimated variables, and the effect on statisticalmodel predictions between the population of wells with a balance between pumping and recharge within 10 percent and the population of all wells were similar. This indicated process-model limitations did not affect the overall findings in the Great Miami River Basin; however, this would be model specific, and prudent use of a process model needs to entail a limitations analysis and, if necessary, alterations to the model.

Ohio↗

USGS2009

An introduction to standardized sampling

It was probably one of the oddest riots in the history of the United States. In Erie, Pennsylvania during 1853. federal marshals were called to restore order during bloody uprisings. A mob of women, equipped with sledgehammers, was tearing up railroad rack to protest standardization of track width (Nesmith 1985). All across the United States, standardization of rail gauges was talking place to improve transportation across the country,but many people did not want consistency. Jobs moving freight from, a train running on one gauge of track to a train running on another gauge were plentiful at this time, and standardization would mean these jobs would disappear. Fortunately, for us today, the riots were quelled and standardization of railroad tack gauges went ahead. The magnificent transportation system of North America was aided by the standardization of rails, contributing to robust economies. Standardization of industrial processes, languages, measurements, and data collection methods has been essential for world progress (Figure 1.1). Today , we are often unaware of the degree of standardization of the most basic elements of our society--from bolts and nuts where thread sizes are standard to computer components that can be used interchangeably to the standard sizes of photos we carry in our wallets or purses. Data collection and presentation are standardized in many disciplines, including medicine, meteorology, geology, and water chemistry. For example, our cholesterol, body temperature, and blood pressure are measured by standard medical tests and compared to averages calculated from the results of the same standard tests for many other people to determine if individuals are higher, lower, or average compared to the population in general. If these diagnostic tests were not standardized, it is unlikely that we would be able to evaluate eve the most basic data about our health. In fact, if standardization was not used in countless other facets of our society our lives would be much more difficult. For data collection purposes, standardization means to collect data in one way so comparisons can be easily made. Although routine data collection has been standardized in many other disciplines, data from freshwater fish sampling across North America have not. Previously, most data collection has been standardized only at local, state, and provincial levels (Bonar and Hubert 2002). Several years ago, when one of the authors (Bonar) was a biologist for a state agency, he was asked to compile as much data as he could about the state's warmwater fish communities to provide information to managers developing fishing regulations. These data had been collected by many biologists over time using different methods, including rotenone, electrofishing, gill netting, and hook-and-line sampling. Data were written carefully on detailed data sheets or in scribbled notes in a biologists's notebook. As you can imagine, these data were a nightmare to compile. However, they were even worse to interpret. How could length-frequency distributions be compared among lakes if the methods used to catch the fish were dissimilar with differing efficiencies in sampling fish of various species and lengths? How could catch per unit effort (CPUE), a common index of population density, be compared when samples were collected one year using fyke nets and the next year by electrofishing? Ultimately, how could one compare if fish population were high, low, or average in growth, body condition, or abundance if there was no compilation of distributions of standard data to facilitate comparison? Months were spent trying to interpret these data, and finally a body of comparable data gathered by similar methods was assimilated. However, much of the nonstandard data had to be discarded--data that had taken thousands of hours to collect but were essentially useless. If all data jhad been collected and recorded in a standard manner, whoch would have required very little extra work, all of these hours of survey effort would not have gone to naught and isights regardimng the fisheries would have been imporived by a larger number of samples.

Book chapter↗

USGS2008

Estimating sediment discharge: Appendix D

Sediment-discharge measurements usually are available on a discrete or periodic basis. However, estimates of sediment transport often are needed for unmeasured periods, such as when daily or annual sediment-discharge values are sought, or when estimates of transport rates for unmeasured or hypothetical flows are required. Selected methods for estimating suspended-sediment, bed-load, bed- material-load, and total-load discharges have been presented in some detail elsewhere in this volume. The purposes of this contribution are to present some limitations and potential pitfalls associated with obtaining and using the requisite data and equations to estimate sediment discharges and to provide guidance for selecting appropriate estimating equations. Records of sediment discharge are derived from data collected with sufficient frequency to obtain reliable estimates for the computational interval and period. Most sediment- discharge records are computed at daily or annual intervals based on periodically collected data, although some partial records represent discrete or seasonal intervals such as those for flood periods. The method used to calculate sediment- discharge records is dependent on the types and frequency of available data. Records for suspended-sediment discharge computed by methods described by Porterfield (1972) are most prevalent, in part because measurement protocols and computational techniques are well established and because suspended sediment composes the bulk of sediment dis- charges for many rivers. Discharge records for bed load, total load, or in some cases bed-material load plus wash load are less common. Reliable estimation of sediment discharges presupposes that the data on which the estimates are based are comparable and reliable. Unfortunately, data describing a selected characteristic of sediment were not necessarily derived—collected, processed, analyzed, or interpreted—in a consistent manner. For example, bed-load data collected with different types of bed-load samplers may not be comparable (Gray et al. 1991; Childers 1999; Edwards and Glysson 1999). The total suspended solids (TSS) analytical method tends to produce concentration data from open-channel flows that are biased low with respect to their paired suspended-sediment concentration values, particularly when sand-size material composes more than about a quarter of the material in suspension. Instantaneous sediment-discharge values based on TSS data may differ from the more reliable product of suspended- sediment concentration values and the same water-discharge data by an order of magnitude (Gray et al. 2000; Bent et al. 2001; Glysson et al. 2000; 2001). An assessment of data comparability and reliability is an important first step in the estimation of sediment discharges. There are two approaches to obtaining values describing sediment loads in streams. One is based on direct measurement of the quantities of interest, and the other on relations developed between hydraulic parameters and sediment- transport potential. In the next sections, the most common techniques for both approaches are briefly addressed.

Book chapter↗

USGS1999

Evaluation of factors that influence estimated zones of transport for six municipal wells in Clark County, Washington

A ground-water flow model was used in conjunction with particle tracking to estimate zones of transport for six municipal well sites in Clark County, Washington. A zone of transport for a well is a three-dimensional volume within a ground-water system that contains all of the ground water that will discharge from that well within a specified time period. All of the zones of transport for a well compose the zone of contribution for the well. Zones of transport and contribution are important considerations in the delineation of wellhead-protection areas. Hydrogeologic factors, such as hydraulic conductivity and porosity, influence the shape and size of the zones of transport, and, therefore, uncertainty in these and other factors can lead to uncertainty in the delineation of the zones of transport. The sensitivity of the zones of transport to uncertainty in selected hydrogeologic factors was evaluated for the six wells. Estimates of the zones of transport were delineated by the U.S. Geological Survey program MODTOOLS from three-dimensional pathlines computed by the U.S. Geological Survey program MODPATH. Input to MODPATH came from steady-state simulations calculated by the U.S. Geological Survey modular three-dimensional finite-difference ground-water flow model, MODFLOW. Three-dimensional modeling is the best method for delineating zones of transport within stratigraphically complex, heterogeneous, anisotropic aquifers that have complex boundary conditions such as streams and multiple, simultaneously discharging wells. In this study, zones of transport were delineated by using simulated particle locations computed from the results of a three-dimensional steady-state regional model for 0-0.5, 0.5-1, 1-5, 5-10, 10-20, and 20-50 year travel times to the selected wells. Zones of transport for a well were delineated by tracking particles along pathlines in the reverse direction of ground-water flow. Sensitivity of the zones of transport to change in the discharge rate of the selected well, porosity, and hydraulic conductivity, as well as to the presence or absence of interfering wells, was evaluated at six well sites to evaluate the effect of uncertainties in these factors on the size and shape of zones of transport. Uncertainty in porosity contributed the most to the uncertainty in delineating the zones of transport. Uncertainty in other factors, such as well discharge rate and horizontal hydraulic conductivity, had measurable effects on the zones of transport, but errors introduced through these factors were less significant. Insight into the causes of the changes in the size and shape of the zones of transport to varying conditions was gained by evaluating the simulated water budget and ground-water levels in the vicinity of the well. Changes in the simulated water budget and ground-water levels provided information to better understand the effects of uncertainties in the data on simulation results.The results of this study suggest that ground-water velocity is the underlying control on the size of the zones of transport. The regional hydraulic gradient is the most significant factor controlling the shape and orientation of the zones of transport. Spatial variation in recharge, discharge, and hydraulic properties can also affect the shape of the zones of transport, however. Underestimation of porosity or overestimation of horizontal hydraulic conductivity leads to overestimation of ground-water velocity and overestimation of the size of zones of transport. Overestimation of porosity or underestimation of horizontal hydraulic conductivity leads to underestimation of ground-water velocity and underestimation of the size of zones of transport. Well discharge rate affects ground-water velocities near the well. Underestimation of discharge (and therefore velocities) will result in underestimation of the size of the zones of transport. The sensitivity of estimated zones of transport to uncertainty in parameters such as porosity and horizontal hydraulic conductivity is a function of the well discharge rate and the proximity of the well to boundaries, such as streams and rivers.

Washington↗

USGS2012

Regression model development and computational procedures to support estimation of real-time concentrations and loads of selected constituents in two tributaries to Lake Houston near Houston, Texas, 2005-9

In December 2005, the U.S. Geological Survey (USGS), in cooperation with the City of Houston, Texas, began collecting discrete water-quality samples for nutrients, total organic carbon, bacteria ( Escherichia coli and total coliform), atrazine, and suspended sediment at two USGS streamflow-gaging stations that represent watersheds contributing to Lake Houston (08068500 Spring Creek near Spring, Tex., and 08070200 East Fork San Jacinto River near New Caney, Tex.). Data from the discrete water-quality samples collected during 2005–9, in conjunction with continuously monitored real-time data that included streamflow and other physical water-quality properties (specific conductance, pH, water temperature, turbidity, and dissolved oxygen), were used to develop regression models for the estimation of concentrations of water-quality constituents of substantial source watersheds to Lake Houston. The potential explanatory variables included discharge (streamflow), specific conductance, pH, water temperature, turbidity, dissolved oxygen, and time (to account for seasonal variations inherent in some water-quality data). The response variables (the selected constituents) at each site were nitrite plus nitrate nitrogen, total phosphorus, total organic carbon, E. coli , atrazine, and suspended sediment. The explanatory variables provide easily measured quantities to serve as potential surrogate variables to estimate concentrations of the selected constituents through statistical regression. Statistical regression also facilitates accompanying estimates of uncertainty in the form of prediction intervals. Each regression model potentially can be used to estimate concentrations of a given constituent in real time. Among other regression diagnostics, the diagnostics used as indicators of general model reliability and reported herein include the adjusted R-squared, the residual standard error, residual plots, and p-values. Adjusted R-squared values for the Spring Creek models ranged from .582–.922 (dimensionless). The residual standard errors ranged from .073–.447 (base-10 logarithm). Adjusted R-squared values for the East Fork San Jacinto River models ranged from .253–.853 (dimensionless). The residual standard errors ranged from .076–.388 (base-10 logarithm). In conjunction with estimated concentrations, constituent loads can be estimated by multiplying the estimated concentration by the corresponding streamflow and by applying the appropriate conversion factor. The regression models presented in this report are site specific, that is, they are specific to the Spring Creek and East Fork San Jacinto River streamflow-gaging stations; however, the general methods that were developed and documented could be applied to most perennial streams for the purpose of estimating real-time water quality data.

Texas↗

USGS2008

Dual-core mass-balance approach for evaluating mercury and 210 Pb atmospheric fallout and focusing to lakes

Determining atmospheric deposition rates of mercury and other contaminants using lake sediment cores requires a quantitative understanding of sediment focusing. Here we present a novel approach that solves mass-balance equations for two cores algebraically to estimate contaminant contributions to sediment from direct atmospheric fallout and from watershed and in-lake focusing. The model is applied to excess 210 Pb and Hg in cores from Hobbs Lake, a high-altitude lake in Wyoming. Model results for excess 210 Pb are consistent with estimates of fallout and focusing factors computed using excess 210 Pb burdens in lake cores and soil cores from the watershed and model results for Hg fallout are consistent with fallout estimated using the soil-core-based 210 Pb focusing factors. The lake cores indicate small increases in mercury deposition beginning in the late 1800s and large increases after 1940, with the maximum at the tops of the cores of 16-20 ??g/m 2 year. These results suggest that global Hg emissions and possibly regional emissions in the western United States are affecting the north-central Rocky Mountains. Hg fallout estimates are generally consistent with fallout reported from an ice core from the nearby Upper Fremont Glacier, but with several notable differences. The model might not work for lakes with complex geometries and multiple sediment inputs, but for lakes with simple geometries, like Hobbs, it can provide a quantitative approach for evaluating sediment focusing and estimating contaminant fallout.

Environmental Science & Technology↗

USGS2023

Towards improved code-based performance objectives for liquefaction hazard analysis

Ground failure due to liquefaction in loose sand deposits poses substantial risks to the built environment, and has caused significant damage in past earthquakes to a wide range of infrastructure. Advances in liquefaction hazard analysis in practice have largely stagnated in recent years; the state of practice remains rooted in simplified procedure s that ignore considerable uncertainties in liquefaction phenomena, and are largely conditional on single-return period ground motions. As a result, they lack any sort of liquefaction-specific design criteria or performance objective. Presented herein is a roadmap for using probabilistic liquefaction hazard analysis (PLHA) to address many of these limitations and improve liquefaction design guidelines. PLHA incorporates hazard contributions from the full ground motion hazard space in conjunction with probabilistic liquefaction models, to produce hazard curves for various types of liquefaction-related demands. In this study, PLHA is utilized to assess the current, implied liquefaction design levels at 76 study sites throughout the U.S. using ASCE 7 guidelines, by computing effective return periods of liquefaction factor of safety FSL, and liquefaction potential index LPI. The results indicate broad inconsistencies in these design levels across different parts of the U.S, with return periods varying from about 300 years in deterministically-capped parts of California, to nearly 3,000 years on the Pacific Northwest coast and in the Charleston Fault zone region. These results are also used to inform potential strategies for establishing consistent, liquefaction-specific design objectives in the future, based on return period averaging methods that weight the importance of a study site according to both the population and relative liquefaction hazard level.

Conference Paper↗

USGS1997

Precipitation-runoff and streamflow-routing models for the Willamette River basin, Oregon

Precipitation-runoff and streamflow-routing models were constructed and assessed as part of a water-quality study of the Willamette River Basin. The study was a cooperative effort between the U.S. Geological Survey (USGS) and the Oregon Department of Environmental Quality (ODEQ) and was coordinated with the USGS National Water-Quality Assessment (NAWQA) study of the Willamette River. Routing models are needed to estimate streamflow so that water-quality constituent loads can be calculated from measured concentrations and so that sources, sinks, and downstream changes in those loads can be identified. Runoff models are needed to estimate ungaged-tributary inflows for routing models and to identify flow contributions from different parts of the basin. The runoff and routing models can be run either separately or together to simulate streamflow at various locations and to examine streamflow contributions from overland flow, shallow-subsurface flow, and ground-water flow. The 11,500-square-mile Willamette River Basin was partitioned into 21 major basins and 253 subbasins. For each subbasin, digital data layers of land use, soils, geology, and topography were combined in a geographic information system (GIS) to define hydrologic response units (HRU's), the basic computational unit for the Precipitation-Runoff Modeling System (PRMS). Spatial data layers were also used to calculate noncalibrated PRMS parameter values. Other PRMS parameter values were obtained from 10 nearby calibrated subbasins of representative location and character. About 760 miles of the Willamette River system were partitioned into 4 main-stem networks and 17 major tributary networks for streamflow routing. Data from time-of-travel studies, discharge measurements, and flood analyses were used to develop equations that related stream cross-sectional area to discharge and stream width to discharge. These relations were derived for all 21 stream networks at approximately 3-mile intervals and used in the Diffusion Analogy Flow model (DAFLOW) in streamflow routing. Ten representative runoff models and 11 network-routing models were calibrated for water years 1972-75 and verified for water years 1976-78. These were the periods with the most complete and widespread streamflow record for the Willamette River Basin. Observed and estimated daily precipitation and daily minimum and maximum air temperature were used as input to the runoff models. The resulting coefficient of determination (R2) for the representative runoff models ranged from 0.69 to 0.93 for the calibration period and from 0.63 to 0.92 for the verification period; absolute errors ranged from 18 to 39 percent and from 27 to 51 percent, respectively. Bias error for the runoff modeling ranged from + 13 to -32 percent. Observed daily streamflow data were used as input to the network-routing models where available, and simulated streamflows from runoff model results were used for ungaged areas. Absolute error for the network-routing models ranged from about 21 percent for the Molalla River model, for which 70 percent of the subbasin was ungaged, to about 4 percent for the Willamette main-stem model (Albany to Salem), for which only 9 percent of the subbasin was ungaged. With an input of current streamflow, precipitation, and air temperature data the combined runoff and routing models can provide current estimates of streamflow at almost 500 locations on the main stem and major tributaries of the Willamette River with a high degree of accuracy. Relative contributions of surface runoff, subsurface flow, and ground-water flow can be assessed for 1 to 10 HRU classes in each of 253 subbasins identified for precipitation-runoff modeling. Model outputs were used with a water-quality model to simulate the movement of dye in the Pudding River as an example

Oregon↗

USGS2019

Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma developed by using streamflow data through 2017

The U.S. Geological Survey (USGS), in cooperation with the Oklahoma Department of Transportation, updated peak-streamflow regression equations for estimating flows with annual exceedance probabilities from 50 to 0.2 percent for the State of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the State by use of a generalized least-squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for stream basins with drainage areas less than 2,510 square miles that are not affected by regulation. The standard model error ranged from 31.28 to 49.32 percent for the different annual exceedance probabilities that were computed. Annual-maximum peak flows observed at 212 USGS streamgages through water year 2017 were used for the regression analysis, excluding the Oklahoma Panhandle region. The USGS StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 212 streamgages in and near Oklahoma are provided in this report. This report contains descriptions of the methods that can be used to estimate peak streamflows at ungaged sites by using estimates from streamgages on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater-retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.

Arkansas, Kansas, Missouri, Oklahoma, Texas↗

USGS1987

Tidal-flow, circulation, and flushing changes caused by dredge and fill in Tampa Bay, Florida

Tampa Bay, Florida, underwent extensive physical changes between 1880 and 1972 because of construction of causeways, islands, channels, and shoreline fills. These changes resulted in a progressive reduction in the quantity of tidal water that enters and leaves the bay. Dredging and filling also changed the magnitude and direction of tidal flows in large parts of the bay. A two-dimensional, finite-difference, hydrodynamic model was used to simulate flood, ebb, and residual transport of both water and a dissolved constituent for the physical conditions that existed in Tampa Bay during 1880 and 1972 and for the conditions that are likely to exist in 1985. The calibrated and verified model was used to hindcast water- and constituent-transport changes caused by construction in the bay between 1880 and 1972. The model was used also to forecast changes that can be expected to occur as a result of a major Federal dredging project scheduled for completion in 1985. The model forecasted transport changes caused by the Federal dredging project to be much less areally extensive than the corresponding transport changes caused by construction in Tampa Bay between 1880 and 1972. Dredging-caused changes of more than 50 percent in flood and ebb transport were computed to occur over only 8 or 9 square miles of the bay's 390-square-mile surface area between 1972 and 1985. The model computed that construction between 1880 and 1972 caused changes of similar magnitude over 58 square miles of the bay. Dredging-caused changes of more than 50 percent in residual transport were computed to occur over 58 square miles of the bay between 1972 and 1985. According to the model simulation, construction between 1880 and 1972 caused changes of similar magnitude over 167 square miles. Computations reveal historical tide-induced circulation patterns. The patterns consist of a series of about 20 interconnected circulatory features that range in diameter from 1 to 6 miles. Dredging- and construction-caused changes in size, position, shape, and intensity of the circulatory features increase tide-induced circulation and flushing throughout most of the bay. As a result of past and projected physical changes, the bay can and will more rapidly transfer waterborne constituents that have landward sources to the Gulf of Mexico. Conversely, the bay can and will more rapidly transfer constituents that have their source in the Gulf into the upper parts of the bay. Model results show that the bay can be functionally subdivided into eight circulation zones. The zones near the entrances to Tampa Bay and the major bay subunits, Hillsborough Bay and Old Tampa Bay, have several times greater average circulation than do adjacent and more landward zones. Circulation generally decreases from the Gulf of Mexico to the head of Hillsborough and Old Tampa Bays, with a striking exception in zone 3 in mid-Tampa Bay. This 10-mile section of the bay has significantly lower average circulation than that of adjacent zones, particularly for conditions in 1880. The section is thought to be a circulation constriction that reduces the potential transport of dissolved and suspended constituents. Circulation in the constricted section was computed as having increased 6 percent because of dredge and fill construction between 1880 and 1972. An additional increase of 21 percent, due to the Federal dredging project, is calculated. With these increases, this zone acts as less of a constriction than it did in 1880. This and other circulation increases may have contributed to increased bay salinity and to more rapid flushing of constituents from the bay to the Gulf of Mexico.

Water Supply Paper↗

USGS1994

Description and application of capture zone delineation for a wellfield at Hilton Head Island, South Carolina

Ground-water capture zone boundaries for individual pumped wells in a confined aquffer were delineated by using groundwater models. Both analytical and numerical (semi-analytical) models that more accurately represent the $round-water-flow system were used. All models delineated 2-dimensional boundaries (capture zones) that represent the areal extent of groundwater contribution to a pumped well. The resultant capture zones were evaluated on the basis of the ability of each model to realistically rapresent the part of the ground-water-flow system that contributed water to the pumped wells. Analytical models used were based on a fixed radius approach, and induded; an arbitrary radius model, a calculated fixed radius model based on the volumetric-flow equation with a time-of-travel criterion, and a calculated fixed radius model derived from modification of the Theis model with a drawdown criterion. Numerical models used induded the 2-dimensional, finite-difference models RESSQC and MWCAP. The arbitrary radius and Theis analytical models delineated capture zone boundaries that compared least favorably with capture zones delineated using the volumetric-flow analytical model and both numerical models. The numerical models produced more hydrologically reasonable capture zones (that were oriented parallel to the regional flow direction) than the volumetric-flow equation. The RESSQC numerical model computed more hydrologically realistic capture zones than the MWCAP numerical model by accounting for changes in the shape of capture zones caused by multiple-well interference. The capture zone boundaries generated by using both analytical and numerical models indicated that the curnmtly used 100-foot radius of protection around a wellhead in South Carolina is an underestimate of the extent of ground-water capture for pumped wetis in this particular wellfield in the Upper Floridan aquifer. The arbitrary fixed radius of 100 feet was shown to underestimate the upgradient contribution of ground-water flow to a pumped well.

South Carolina↗

USGS2010

Constituent concentrations, loads, and yields to Beaver Lake, Arkansas, water years 1999-2008

Beaver Lake is a large, deep-storage reservoir used as a drinking-water supply and considered a primary watershed of concern in the State of Arkansas. As such, information is needed to assess water quality, especially nutrient enrichment, nutrient-algal relations, turbidity, and sediment issues within the reservoir system. Water-quality samples were collected at three main inflows to Beaver Lake: the White River near Fayetteville, Richland Creek at Goshen, and War Eagle Creek near Hindsville. Water-quality samples collected over the period represented different flow conditions (from low to high). Constituent concentrations, flow-weighted concentrations, loads, and yields from White River, Richland Creek, and War Eagle Creek to Beaver Lake for water years 1999-2008 were documented for this report. Constituents include total ammonia plus organic nitrogen, dissolved nitrite plus nitrate nitrogen, dissolved orthophosphorus (soluble reactive phosphorus), total phosphorus, total nitrogen, dissolved organic carbon, total organic carbon, and suspended sediment. Linear regression models developed by computer program S-LOADEST were used to estimate loads for each constituent for the 10-year period at each station. Constituent yields and flow-weighted concentrations for each of the three stations were calculated for the study. Constituent concentrations and loads and yields varied with time and varied among the three tributaries contributing to Beaver Lake. These differences can result from differences in precipitation, land use, contributions of nutrients from point sources, and variations in basin size. Load and yield estimates varied yearly during the study period, water years 1999-2008, with the least nutrient and sediment load and yields generally occurring in water year 2006, and the greatest occurring in water year 2008, during a year with record amounts of precipitation. Flow-weighted concentrations of most constituents were greatest at War Eagle Creek near Hindsville than White River near Fayetteville and Richland Creek at Goshen. Loads and yields of most constituents were greater at the War Eagle Creek and White River stations than at the Richland Creek Station.

Scientific Investigations Report↗

USGS1999

A temporal and spatial analysis of ground-water levels for effective monitoring in Huron County, Michigan

Quarterly water-level measurements were analyzed to assess the effectiveness of a monitoring network of 26 wells in Huron County, Michigan. Trends were identified as constant levels and autoregressive components were computed at all wells on the basis of data collected from 1993 to 1997, using structural time series analysis. Fixed seasonal components were identified at 22 wells and outliers were identified at 23 wells. The 95- percent confidence intervals were forecast for water-levels during the first and second quarters of 1998. Intervals in the first quarter were consistent with 92.3 percent of the measured values. In the second quarter, measured values were within the forecast intervals only 65.4 percent of the time. Unusually low precipitation during the second quarter is thought to have contributed to the reduced reliability of the second-quarter forecasts. Spatial interrelations among wells were investigated on the basis of the autoregressive components, which were filtered to create a set of innovation sequences that were temporally uncorrelated. The empirical covariance among the innovation sequences indicated both positive and negative spatial interrelations. The negative covariance components are considered to be physically implausible and to have resulted from random sampling error. Graphical modeling, a form of multivariate analysis, was used to model the covariance structure. Results indicate that only 29 of the 325 possible partial correlations among the water-level innovations were statistically significant. The model covariance matrix, corresponding to the model partial correlation structure, contained only positive elements. This model covariance was sequentially partitioned to compute a set of partial covariance matrices that were used to rank the effectiveness of the 26 monitoring wells from greatest to least. Results, for example, indicate that about 50 percent of the uncertainty of the water-level innovations currently monitored by the 26- well network could be described by the 6 most effective wells.

Michigan↗

USGS2013

Current research issues related to post-wildfire runoff and erosion processes

Research into post-wildfire effects began in the United States more than 70 years ago and only later extended to other parts of the world. Post-wildfire responses are typically transient, episodic, variable in space and time, dependent on thresholds, and involve multiple processes measured by different methods. These characteristics tend to hinder research progress, but the large empirical knowledge base amassed in different regions of the world suggests that it should now be possible to synthesize the data and make a substantial improvement in the understanding of post-wildfire runoff and erosion response. Thus, it is important to identify and prioritize the research issues related to post-wildfire runoff and erosion. Priority research issues are the need to: (1) organize and synthesize similarities and differences in post-wildfire responses between different fire-prone regions of the world in order to determine common patterns and generalities that can explain cause and effect relations; (2) identify and quantify functional relations between metrics of fire effects and soil hydraulic properties that will better represent the dynamic and transient conditions after a wildfire; (3) determine the interaction between burned landscapes and temporally and spatially variable meso-scale precipitation, which is often the primary driver of post-wildfire runoff and erosion responses; (4) determine functional relations between precipitation, basin morphology, runoff connectivity, contributing area, surface roughness, depression storage, and soil characteristics required to predict the timing, magnitudes, and duration of floods and debris flows from ungaged burned basins; and (5) develop standard measurement methods that will ensure the collection of uniform and comparable runoff and erosion data. Resolution of these issues will help to improve conceptual and computer models of post-wildfire runoff and erosion processes.

Earth-Science Reviews↗

USGS1964

Life history of lake herring in Lake Superior

The average annual commercial catch of lake herring (Coregonus artedi) in U.S. waters of Lake Superior was nearly 12 million pounds in 1929-61. This production contributed 62.4 percent of the total U.S. take of lake herring for the Great Lakes. About 90 percent of the annual catch is taken from small-mesh gill nets during the November-December spawning season. The life-history studies were based on 12,187 fish collected in 1950-62; past growth was computed for 3,779 specimens collected from commercial landings at: Duluth, Minn.; Bayfield, Wis.; and Portage Entry and Marquette, Mich.

Fishery Bulletin↗

USGS1976

Model for evaluating the effects of dikes on the water and salt balance of Great Salt Lake, Utah

A model was developed for predicting the water and salt budget for various diking options in Great Salt Lake. The water budget was computed for 1-month intervals during a base period of 1931-73. The storage change (AS) during each month of the base period was computed from a budget of surface inflow (Is), groundwater inflow (Ig), precipitation on the lake surface (Ip), and outflow from evaporation (Oe), where AS = Is + Ig +Ip - Oe. The total annual inflow to Great Salt Lake ranged from about 1.5 to 5 million acre-feet (1,849.5 to 6,165.0 cubic hectometres). The Bear River contributes the largest percentage of the measured surface inflow. The total annual outflow from the lake (evaporation) ranged from about 2.2 to 4.0 million acre-feet (2,712.6 to 4,932.0 cubic hectometres) during 1931-73. The average annual evaporation was 2.98 million acrefeet (3,674.3 cubic hectometres) or 45 inches (1,143 millimetres) per year. The model provides for nine diking options. These include combinations of eight areas east of a line joining Antelope Island, Fremont Island, and the Promontory Mountains. Another option includes the part of Great Salt Lake that lies north of the Southern Pacific Transportation Co. causeway, which divides the main body of the lake into north and south parts. The model treats the salt balance of the diked areas from the standpoint of an inflow-outflow balance with complete mixing, and no allowances are made for any stratification or chemical changes due to interaction with the sediments or solution of entrapped brines or residual salts. Because the degree of inaccuracy created by these assumptions is not known, the concentrations predicted by the model should be regarded not as absolute but as relative indexes by which to compare various diking alternatives.

Utah↗

USGS1991

Source of atrazine and desethylatrazine in a river, during base flow

A budget of atrazine and desethylatrazine loads was computed for a 116-kilometer reach of the Cedar River in Iowa to determine where these compounds enter the river during base-flow conditions. Loads were determined by measuring discharge and concentrations of the compounds for four main-stem sites along the Cedar River and for 27 tributaries on September 20-22, 1989. Tributaries contributed 17 percent of the increased atrazine load and 24 percent of the increased desethylatrazine load measured between the extreme upstream and downstream sites on the Cedar River. The remaining 76 to 83 percent of the increased loads were attributed to input along the groundwater's main stem. The ground-water samples were collected at depths from 1 to 2 meters beneath the river bottom where the ground water was determined to be moving toward the river. The sources of atrazine and desethylatrazine detected in the ground water may include bank storage of river water or ground-water recharge originating at some distance from the river.

Conference Paper↗