USGS Science⌕ Search

SEARCH · USGS Science

Results for “Bulletin”

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 379 records · Page 21Linked to original sources
USGS1919

Deposits of Claiborne and Jackson age in Georgia

In 1911 the Geological Survey of Georgia published as Bulletin 26 a "Preliminary report on the geology of the Coastal Plain of Georgia," by Otto Veatch and Lloyd William Stephenson, prepared in cooperation with the United States Geological Survey under the supervision of T. Wayland Vaughan, a geologist in charge of Coastal Plain investigations, who contributed the determinations of the invertebrate fossils of the Tertiary and Quaternary formations. Although this report constituted a decided advance in our knowledge of the geology of the Coastal Plain of Georgia, it was admittedly of reconnaissance character, and corrections and additions to it were to be expected. During the last few years field work has been prosecuted vigorously in the Coastal Plain of Georgia, and the additional information thus accumulated throws light upon certain problems of stratigraphy left unsolved by Veatch and Stephenson and alters considerably some of their correlations. The object of the present paper is to present the new evidence regarding the age and correlation of the Eocene formations of Georgia and to revise in accordance with present knowledge the descriptions of the deposits of Claiborne and Jackson age.

Georgia↗
USGS1973

United States mineral resources

The work on this volume began in January 1972, but in a broader sense its production began many years ago. The chapters were written by geologists most of whom have had many years of experience studying the geology of mineral deposits, and more particularly the commodities about which they have written here. A total of nearly 2,300 man-years of professional experience in the geology of mineral resources is represented by the authors of the volume, and about 30 man-years went directly into its preparation. Each chapter contains not only a synthesis of the state of knowledge of the geology of the commodity, but also an appraisal of the known resources, and an examination of the geologic possibilities for finding additional deposits. In January 1972, responsibility for the preparation of the volume was assigned to us as co-editors, and we were given a tentative list of commodities and authors. We provided each author with a suggested outline of general topics to be covered, and some guidelines as to scope and philosophy of approach, but beyond that we avoided any attempt to fit each chapter into a stereotype. Moreover, the types of commodities range from the major metals and industrial minerals such as copper, silver, and fluorspar, which have been the subject of geologic research for years, to other commodities that are of such varied geologic nature (such as pigments or gemstones) or of such minor present importance (such as scandium or thallium) that they cannot be treated from the same viewpoint as the major minerals. The chapters range, therefore, from comprehensive summary reports to general essays that reflect the individuality of the authors as well as the variation among commodities. Throughout the book the emphasis is on geology, but each chapter contains some summary information on uses, technology, and economics. These summaries are not meant to be exhaustive, however, and additional details are in the 1970 edition of "Mineral Facts and Problems" (Bulletin 650 of the U.S. Bureau of Mines) ; indeed, we regard that book and the present volume as being complementary. In the examination of the geologic possibilities for finding new deposits-in many respects the principal innovative contributions of this volume-we asked the authors to frankly apply the limits of their ingenuity and not only to summarize current theories but also to express their own intuitive ideas, however speculative and unconventional they may seem, that have come from years of study devoted to the origin of mineral deposits. Readers will see that some authors have speculated more courageously than others. In any case, we believe readers will find all the chapters interesting, and many stimulating; and a few we believe can be frankly characterized as intellectually exciting. Most chapters include a section on prospecting techniques, and a summary of geologic or related problems on which the authors believe research might be most fruitful in the continuing efforts to find new resources. An integral part of the book is the bibliographic material cited at the conclusion of each chapter, in lieu of repetition of detailed descriptions already in print. Index and "spot" maps are not included in most chapters because they are available elsewhere, and in many cases with more detail than could possibly be included here. Maps showing the distribution of known deposits of many commodities in the United States are available in the Mineral Resource (MR) map series of the U.S. Geological Survey and in the National Atlas of the United States. The first three chapters deal not with resources of specific commodities but with general information that is pertinent to the study of mineral resources. In the introductory chapter we discuss the purposes of the book, the distinctions between reserves and various categories of resources, and some general conclusions drawn from our view of the book in its entirety. In the second chapter V. E. McKelvey discusses the problems of mineral-resource estimates and public policy. In the third chapter, R. L. Erickson discusses some new points of view on the relation of reserves and resources to the crustal abundance of elements. We acknowledge with thanks the cooperation of our colleagues in all phases of the preparation of this volume. Whatever success the book may attain is due entirely to a total effort. A paragraph of acknowledgment originally submitted as a part of the chapter on "Nuclear fuels" is given here instead because we feel it applied to all chapters: "The writers have drawn freely from published information, not all of which is cited, and from their colleagues, none of whom are given specific credit. The reader should be aware that the paper could not have been written without these sources." We extend specific thanks to Michael Fleischer for preparation of summaries of geochemical information that are included in many chapters.

Professional Paper↗
USGS2005

Estimating the magnitude and frequency of floods for streams in west-central Florida, 2001

Flood discharges were estimated for recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years for 94 streamflow stations in west-central Florida. Most of the stations are located within the 10,000 square-mile, 16-county area that forms the Southwest Florida Water Management District. All stations had at least 10 years of homogeneous record, and none have flood discharges that are significantly affected by regulation or urbanization. Guidelines established by the U.S. Water Resources Council in Bulletin 17B were used to estimate flood discharges from gaging station records. Multiple linear regression analysis was then used to mathematically relate estimates of flood discharge for selected recurrence intervals to explanatory basin characteristics. Contributing drainage area, channel slope, and the percent of total drainage area covered by lakes (percent lake area) were the basin characteristics that provided the best regression estimates. The study area was subdivided into four geographic regions to further refine the regression equations. Region 1 at the northern end of the study area includes large rivers that are characteristic of the rolling karst terrain of northern Florida. Only a small part of Region 1 lies within the boundaries of the Southwest Florida Water Management District. Contributing drainage area and percent lake area were the most statistically significant basin characteristics in Region 1; the prediction error of the regression equations varied with the recurrence interval and ranged from 57 to 69 percent. In the three other regions of the study area, contributing drainage area, channel slope, and percent lake area were the most statistically significant basin characteristics, and are the three characteristics that can be used to best estimate the magnitude and frequency of floods on most streams within the Southwest Florida Water Management District. The Withlacoochee River Basin dominates Region 2; the prediction error of the regression models in the region ranged from 65 to 68 percent. The basins that drain into the northern part of Tampa Bay and the upper reaches of the Peace River Basin are in Region 3, which had prediction errors ranging from 54 to 74 percent. Region 4, at the southern end of the study area, had prediction errors that ranged from 40 to 56 percent. Estimates of flood discharge become more accurate as longer periods of record are used for analyses; results of this study should be used in lieu of results from earlier U.S. Geological Survey studies of flood magnitude and frequency in west-central Florida. A comparison of current results with earlier studies indicates that use of a longer period of record with additional high-water events produces substantially higher flood-discharge estimates for many gaging stations. Another comparison indicates that the use of a computed, generalized skew in a previous study in 1979 tended to overestimate flood discharges.

Scientific Investigations Report↗
USGS2006

Methods for Adjusting U.S. Geological Survey Rural Regression Peak Discharges in an Urban Setting

A study was conducted of 78 U.S. Geological Survey gaged streams that have been subjected to varying degrees of urbanization over the last three decades. Flood-frequency analysis coupled with nonlinear regression techniques were used to generate a set of equations for converting peak discharge estimates determined from rural regression equations to a set of peak discharge estimates that represent known urbanization. Specifically, urban regression equations for the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year return periods were calibrated as a function of the corresponding rural peak discharge and the percentage of impervious area in a watershed. The results of this study indicate that two sets of equations, one set based on imperviousness and one set based on population density, performed well. Both sets of equations are dependent on rural peak discharges, a measure of development (average percentage of imperviousness or average population density), and a measure of homogeneity of development within a watershed. Average imperviousness was readily determined by using geographic information system methods and commonly available land-cover data. Similarly, average population density was easily determined from census data. Thus, a key advantage to the equations developed in this study is that they do not require field measurements of watershed characteristics as did the U.S. Geological Survey urban equations developed in an earlier investigation. During this study, the U.S. Geological Survey PeakFQ program was used as an integral tool in the calibration of all equations. The scarcity of historical land-use data, however, made exclusive use of flow records necessary for the 30-year period from 1970 to 2000. Such relatively short-duration streamflow time series required a nonstandard treatment of the historical data function of the PeakFQ program in comparison to published guidelines. Thus, the approach used during this investigation does not fully comply with the guidelines set forth in U.S. Geological Survey Bulletin 17B, and modifications may be needed before it can be applied in practice.

Scientific Investigations Report↗
USGS2009

Estimation of Flood Discharges at Selected Recurrence Intervals for Streams in New Hampshire

This report provides estimates of flood discharges at selected recurrence intervals for streamgages in and adjacent to New Hampshire and equations for estimating flood discharges at recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years for ungaged, unregulated, rural streams in New Hampshire. The equations were developed using generalized least-squares regression. Flood-frequency and drainage-basin characteristics from 117 streamgages were used in developing the equations. The drainage-basin characteristics used as explanatory variables in the regression equations include drainage area, mean April precipitation, percentage of wetland area, and main channel slope. The average standard error of prediction for estimating the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence interval flood discharges with these equations are 30.0, 30.8, 32.0, 34.2, 36.0, 38.1, and 43.4 percent, respectively. Flood discharges at selected recurrence intervals for selected streamgages were computed following the guidelines in Bulletin 17B of the U.S. Interagency Advisory Committee on Water Data. To determine the flood-discharge exceedence probabilities at streamgages in New Hampshire, a new generalized skew coefficient map covering the State was developed. The standard error of the data on new map is 0.298. To improve estimates of flood discharges at selected recurrence intervals for 20 streamgages with short-term records (10 to 15 years), record extension using the two-station comparison technique was applied. The two-station comparison method uses data from a streamgage with long-term record to adjust the frequency characteristics at a streamgage with a short-term record. A technique for adjusting a flood-discharge frequency curve computed from a streamgage record with results from the regression equations is described in this report. Also, a technique is described for estimating flood discharge at a selected recurrence interval for an ungaged site upstream or downstream from a streamgage using a drainage-area adjustment. The final regression equations and the flood-discharge frequency data used in this study will be available in StreamStats. StreamStats is a World Wide Web application providing automated regression-equation solutions for user-selected sites on streams.

Scientific Investigations Report↗
USGS2010

Historical changes in annual peak flows in Maine and implications for flood-frequency analyses

Flood-frequency analyses use statistical methods to compute peak streamflows for selected recurrence intervals— the average number of years between peak flows that are equal to or greater than a specified peak flow. Analyses are based on annual peak flows at a stream. It has long been assumed that the annual peak streamflows used in these computations were stationary (non-changing) over very long periods of time, except in river basins subject to direct effects of human activities, such as urbanization and regulation. Because of the potential effects of global warming on peak flows, the assumption of peak-flow stationarity has recently been questioned. Maine has many streamgages with 50 to 105 years of recorded annual peak streamflows. In this study, this long-term record has been tested for historical flood-frequency stationarity, to provide some insight into future flood frequency. Changes over time in annual instantaneous peak streamflows at 28 U.S. Geological Survey streamgages with long-term data (50 or more years) and relatively complete records were investigated by examining linear trends for each streamgage’s period of record. None of the 28 streamgages had more than 5 years of missing data. Eight streamgages have substantial streamflow regulation. Because previous studies have suggested that changes over time may have occurred as a step change around 1970, step changes between each streamgage’s older record (start year to 1970) and newer record (1971 to 2006) also were computed. The median change over time for all 28 streamgages is an increase of 15.9 percent based on a linear change and an increase of 12.4 percent based on a step change. The median change for the 20 unregulated streamgages is slightly higher than for all 28 streamgages; it is 18.4 percent based on a linear change and 15.0 percent based on a step change. Peak flows with 100- and 5-year recurrence intervals were computed for the 28 streamgages using the full annual peak-flow record and multiple sub-periods of that record using the guidelines (Bulletin 17B) of the Interagency Advisory Committee on Water Data. Magnitudes of 100- and 5-year peak flows computed from sub-periods then were compared to those computed from the full period. Sub-periods of 30 years with starting years staggered by 10 years were evaluated (1907–36, 1917–46, 1927–56, 1937–66, 1947–76, 1957–86, 1967–96, and 1977–2006). Two other sub-periods were evaluated using older data (start-of-record to 1970) and newer data (1971 to 2006). The 5-year peak flow is used to represent small and relatively frequent flood flows in Maine, whereas the 100-year peak flow is used to represent large flood flows. The 1967–96 sub-period generated the highest 100- and 5-year peak flows overall when compared to peak flows based on the full period of record; the median difference for all 28 streamgages is 8 percent for 100- and 5-year peak flows. The 1977–2006 and 1971–2006 sub-periods also generated 100- and 5-year peak flows higher than peak flows based on the full period of record, but not as high as the peak flows based on the 1967–96 sub-period. The 1937–66 sub-period generated the lowest 100- and 5-year peak flows overall. The median difference from full-period peak flows is -11 percent for 100-year peak flows and -8 percent for 5-year peak flows. Overall, differences between peak flows based on the sub-periods and those based on the full periods, generated using the 20 unregulated streamgages, are similar to differences using all 28 streamgages. Increases in the 5- and 100-year peak flows based on recent years of record are, in general, modest when compared to peak flows based on complete periods of record. The highest peak flows are based on the 1967–96 sub-period rather than the most recent sub-period (1977-2006). Peak flows for selected recurrence intervals are sensitive to very high peak flows that may occur once in a century or even less frequently. It is difficult, therefore, to determine which approach will produce the most reliable future estimates of peak flows for selected recurrence intervals, using only recent years of record or the traditional method using the entire historical period. One possible conservative approach to computing peak flows of selected recurrence intervals would be to compute peak flows using recent annual peak flows and the entire period of record, then choose the higher computed value. Whether recent or entire periods of record are used to compute peak flows of selected recurrence intervals, the results of this study highlight the importance of using recent data in the computation of the peak flows. The use of older records alone could result in underestimation of peak flows, particularly peak flows with short recurrence intervals, such as the 5-year peak flows.

Maine↗
USGS2012

Magnitude of flood flows for selected annual exceedance probabilities in Rhode Island through 2010

Heavy persistent rains from late February through March 2010 caused severe widespread flooding in Rhode Island that set or nearly set record flows and water levels at many long-term streamgages in the State. In response, the U.S. Geological Survey, in partnership with the Federal Emergency Management Agency, conducted a study to update estimates of flood magnitudes at streamgages and regional equations for estimating flood flows at ungaged locations. This report provides information needed for flood plain management, transportation infrastructure design, flood insurance studies, and other purposes that can help minimize future flood damages and risks. The magnitudes of floods were determined from the annual peak flows at 43 streamgages in Rhode Island (20 sites), Connecticut (14 sites), and Massachusetts (9 sites) using the standard Bulletin 17B log-Pearson type III method and a modification of this method called the expected moments algorithm (EMA) for 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability (AEP) floods. Annual-peak flows were analyzed for the period of record through the 2010 water year; however, records were extended at 23 streamgages using the maintenance of variance extension (MOVE) procedure to best represent the longest period possible for determining the generalized skew and flood magnitudes. Generalized least square regression equations were developed from the flood quantiles computed at 41 streamgages (2 streamgages in Rhode Island with reported flood quantiles were not used in the regional regression because of regulation or redundancy) and their respective basin characteristics to estimate magnitude of floods at ungaged sites. Of 55 basin characteristics evaluated as potential explanatory variables, 3 were statistically significant—drainage area, stream density, and basin storage. The pseudo-coefficient of determination (pseudo- R2 ) indicates these three explanatory variables explain 95 to 96 percent of the variance in the flood magnitudes from 20- to 0.2-percent AEPs. Estimates of uncertainty of the at-site and regression flood magnitudes are provided and were combined with their respective estimated flood quantiles to improve estimates of flood flows at streamgages. This region has a long history of urban development, which is considered to have an important effect on flood flows. This study includes basins that have an impervious area ranging from 0.5 to 37 percent. Although imperviousness provided some explanatory power in the regression, it was not statistically significant at the 95-percent confidence level for any of the AEPs examined. Influence of urbanization on flood flows indicates a complex interaction with other characteristics that confounds a statistical explanation of its effects. Standard methods for calculating magnitude of floods for given AEP are based on the assumption of stationarity, that is, the annual peak flows exhibit no significant trend over time. A subset of 16 streamgages with 70 or more years of unregulated systematic record indicates all but 4 streamgages have a statistically significant positive trend at the 95-percent confidence level; three of these are statistically significant at about the 90-percent confidence level or above. If the trend continues linearly in time, the estimated magnitude of floods for any AEP, on average, will increase by 6, 13, and 21 percent in 10, 20, and 30 years' time, respectively. In 2010, new peaks of record were set at 18 of the 21 active streamgages in Rhode Island. The updated flood frequency analysis indicates the peaks at these streamgages ranged from 2- to 0.2-percent AEP. Many streamgages in the State peaked at a 0.5- and 0.2-percent AEP, except for streamgages in the Blackstone River Basin, which peaked from a 4- to 2-percent AEP.

Rhode Island↗
USGS2012

Development of regional skews for selected flood durations for the Central Valley Region, California, based on data through water year 2008

Flood-frequency information is important in the Central Valley region of California because of the high risk of catastrophic flooding. Most traditional flood-frequency studies focus on peak flows, but for the assessment of the adequacy of reservoirs, levees, other flood control structures, sustained flood flow (flood duration) frequency data are needed. This study focuses on rainfall or rain-on-snow floods, rather than the annual maximum, because rain events produce the largest floods in the region. A key to estimating flood-duration frequency is determining the regional skew for such data. Of the 50 sites used in this study to determine regional skew, 28 sites were considered to have little to no significant regulated flows, and for the 22 sites considered significantly regulated, unregulated daily flow data were synthesized by using reservoir storage changes and diversion records. The unregulated, annual maximum rainfall flood flows for selected durations (1-day, 3-day, 7-day, 15-day, and 30-day) for all 50 sites were furnished by the U.S. Army Corps of Engineers. Station skew was determined by using the expected moments algorithm program for fitting the Pearson Type 3 flood-frequency distribution to the logarithms of annual flood-duration data. Bayesian generalized least squares regression procedures used in earlier studies were modified to address problems caused by large cross correlations among concurrent rainfall floods in California and to address the extensive censoring of low outliers at some sites, by using the new expected moments algorithm for fitting the LP3 distribution to rainfall flood-duration data. To properly account for these problems and to develop suitable regional-skew regression models and regression diagnostics, a combination of ordinary least squares, weighted least squares, and Bayesian generalized least squares regressions were adopted. This new methodology determined that a nonlinear model relating regional skew to mean basin elevation was the best model for each flood duration. The regional-skew values ranged from -0.74 for a flood duration of 1-day and a mean basin elevation less than 2,500 feet to values near 0 for a flood duration of 7-days and a mean basin elevation greater than 4,500 feet. This relation between skew and elevation reflects the interaction of snow and rain, which increases with increased elevation. The regional skews are more accurate, and the mean squared errors are less than in the Interagency Advisory Committee on Water Data's National skew map of Bulletin 17B.

California↗
USGS2013

Metal prices in the United States through 2010

This report, which updates and revises the U.S. Geological Survey (USGS) (1999) publication, “Metal Prices in the United States Through 1998,” presents an extended price history for a wide range of metals available in a single document. Such information can be useful for the analysis of mineral commodity issues, as well as for other purposes. The chapter for each mineral commodity includes a graph of annual current and constant dollar prices for 1970 through 2010, where available; a list of significant events that affected prices; a brief discussion of the metal and its history; and one or more tables that list current dollar prices. In some cases, the metal prices presented herein are for some alternative form of an element or, instead of a price, a value, such as the value for an import as appraised by the U.S. Customs Service. Also included are the prices for steel, steel scrap, and iron ore—steel because of its importance to the elements used to alloy with it, and steel scrap and iron ore because of their use in steelmaking. A few minor metals, such as calcium, potassium, sodium, strontium, and thorium, for which price histories were insufficient, were excluded. The annual prices given may be averages for the year, yearend prices, or some other price as appropriate for a particular commodity. Certain trade journals have been the source of much of this price information—American Metal Market, ICIS Chemical Business, Engineering and Mining Journal, Industrial Minerals, Metal Bulletin, Mining Journal, Platts Metals Week, Roskill Information Services Ltd. commodity reports, and Ryan’s Notes. Price information also is available in minerals information publications of the USGS (1880–1925, 1996–present) and the U.S. Bureau of Mines (1926–95), such as Mineral Commodity Summaries, Mineral Facts and Problems, Mineral Industry Surveys, and Minerals Yearbook. In addition to prices themselves, these journals and publications contain information relevant to prices, which has been helpful in the preparation of this publication. Prices in this report have been graphed in 1992 constant dollars to show the effects of inflation as measured by the U.S. Bureau of Labor Statistics Consumer Price Index for All Urban Consumers, a widely used measure of overall inflation in the United States. These prices are not tabulated, but a table of the deflators used is given in an appendix. Constant dollar prices can be used to show how prices that producers receive would have less purchasing power.

Scientific Investigations Report↗
USGS2013

Streamflow statistics for unregulated and regulated conditions for selected locations on the Yellowstone, Tongue, and Powder Rivers, Montana, 1928-2002

Major floods in 1996 and 1997 on the Yellowstone River in Montana intensified public debate over the effects of human activities on the Yellowstone River. In 1999, the Yellowstone River Conservation District Council was formed to address conservation issues on the river. The Yellowstone River Conservation District Council partnered with the U.S. Army Corps of Engineers to conduct a cumulative-effects study on the main stem of the Yellowstone River. The cumulative-effects study is intended to provide a basis for future management decisions in the watershed. Streamflow statistics, such as flow-frequency and flow-duration data calculated for unregulated and regulated streamflow conditions, are a necessary component of the cumulative effects study. The U.S. Geological Survey, in cooperation with the Yellowstone River Conservation District Council and the U.S. Army Corps of Engineers, calculated streamflow statistics for unregulated and regulated conditions for the Yellowstone, Tongue, and Powder Rivers for the 1928–2002 study period. Unregulated streamflow represents flow conditions that might have occurred during the 1928–2002 study period if there had been no water-resources development in the Yellowstone River Basin. Regulated streamflow represents estimates of flow conditions during the 1928–2002 study period if the level of water-resources development existing in 2002 was in place during the entire study period. Peak-flow frequency estimates for regulated and unregulated streamflow were developed using methods described in Bulletin 17B. High-flow frequency and low-flow frequency data were developed for regulated and unregulated streamflows from the annual series of highest and lowest (respectively) mean flows for specified n-day consecutive periods within the calendar year. Flow-duration data, and monthly and annual streamflow characteristics, also were calculated for the unregulated and regulated streamflows.

Montana;North Dakota;Wyoming↗
USGS2013

Streamflow monitoring and statistics for development of water rights claims for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, Idaho, 2012

The U.S. Geological Survey, in cooperation with the Bureau of Land Management (BLM), collected streamflow data in 2012 and estimated streamflow statistics for stream segments designated "Wild," "Scenic," or "Recreational" under the National Wild and Scenic Rivers System in the Owyhee Canyonlands Wilderness in southwestern Idaho. The streamflow statistics were used by BLM to develop and file a draft, federal reserved water right claim in autumn 2012 to protect federally designated "outstanding remarkable values" in the stream segments. BLM determined that the daily mean streamflow equaled or exceeded 20 and 80 percent of the time during bimonthly periods (two periods per month) and the bankfull streamflow are important streamflow thresholds for maintaining outstanding remarkable values. Prior to this study, streamflow statistics estimated using available datasets and tools for the Owyhee Canyonlands Wilderness were inaccurate for use in the water rights claim. Streamflow measurements were made at varying intervals during February–September 2012 at 14 monitoring sites; 2 of the monitoring sites were equipped with telemetered streamgaging equipment. Synthetic streamflow records were created for 11 of the 14 monitoring sites using a partial‑record method or a drainage-area-ratio method. Streamflow records were obtained directly from an operating, long-term streamgage at one monitoring site, and from discontinued streamgages at two monitoring sites. For 10 sites analyzed using the partial-record method, discrete measurements were related to daily mean streamflow at a nearby, telemetered “index” streamgage. Resulting regression equations were used to estimate daily mean and annual peak streamflow at the monitoring sites during the full period of record for the index sites. A synthetic streamflow record for Sheep Creek was developed using a drainage-area-ratio method, because measured streamflows did not relate well to any index site to allow use of the partial-record method. The synthetic and actual daily mean streamflow records were used to estimate daily mean streamflow that was exceeded 80, 50, and 20 percent of the time (80-, 50-, and 20-percent exceedances) for bimonthly and annual periods. Bankfull streamflow statistics were calculated by fitting the synthetic and actual annual peak streamflow records to a log Pearson Type III distribution using Bulletin 17B guidelines in the U.S. Geological Survey PeakFQ program. The coefficients of determination (R 2 ) for the regressions between the monitoring and index sites ranged from 0.74 for Wickahoney Creek to 0.98 for the West Fork Bruneau River and Deep Creek. Confidence in computed streamflow statistics is highest among other sites for the East Fork Owyhee River and the West Fork Bruneau River on the basis of regression statistics, visual fit of the related data, and the range and number of streamflow measurements. Streamflow statistics for sites with the greatest uncertainty included Big Jacks, Little Jacks, Cottonwood, Wickahoney, and Sheep Creeks. The uncertainty in computed streamflow statistics was due to a number of factors which included the distance of index sites relative to monitoring sites, relatively low streamflow conditions that occurred during the study, and the limited number and range of streamflow measurements. However, the computed streamflow statistics are considered the best possible estimates given available datasets in the remote study area. Streamflow measurements over a wider range of hydrologic and climatic conditions would improve the relations between streamflow characteristics at monitoring and index sites. Additionally, field surveys are needed to verify if the streamflows selected for the water rights claims are sufficient for maintaining outstanding remarkable values in the Wild and Scenic rivers included in the study.

Idaho;Nevada;Oregon↗
USGS2014

Evaluation of the expected moments algorithm and a multiple low-outlier test for flood frequency analysis at streamgaging stations in Arizona

Flooding is among the costliest natural disasters in terms of loss of life and property in Arizona, which is why the accurate estimation of flood frequency and magnitude is crucial for proper structural design and accurate floodplain mapping. Current guidelines for flood frequency analysis in the United States are described in Bulletin 17B (B17B), yet since B17B’s publication in 1982 (Interagency Advisory Committee on Water Data, 1982), several improvements have been proposed as updates for future guidelines. Two proposed updates are the Expected Moments Algorithm (EMA) to accommodate historical and censored data, and a generalized multiple Grubbs-Beck (MGB) low-outlier test. The current guidelines use a standard Grubbs-Beck (GB) method to identify low outliers, changing the determination of the moment estimators because B17B uses a conditional probability adjustment to handle low outliers while EMA censors the low outliers. B17B and EMA estimates are identical if no historical information or censored or low outliers are present in the peak-flow data. EMA with MGB (EMA-MGB) test was compared to the standard B17B (B17B-GB) method for flood frequency analysis at 328 streamgaging stations in Arizona. The methods were compared using the relative percent difference (RPD) between annual exceedance probabilities (AEPs), goodness-of-fit assessments, random resampling procedures, and Monte Carlo simulations. The AEPs were calculated and compared using both station skew and weighted skew. Streamgaging stations were classified by U.S. Geological Survey (USGS) National Water Information System (NWIS) qualification codes, used to denote historical and censored peak-flow data, to better understand the effect that nonstandard flood information has on the flood frequency analysis for each method. Streamgaging stations were also grouped according to geographic flood regions and analyzed separately to better understand regional differences caused by physiography and climate. The B17B-GB and EMA-MGB RPD-boxplot results showed that the median RPDs across all streamgaging stations for the 10-, 1-, and 0.2-percent AEPs, computed using station skew, were approximately zero. As the AEP flow estimates decreased (that is, from 10 to 0.2 percent AEP) the variability in the RPDs increased, indicating that the AEP flow estimate was greater for EMA-MGB when compared to B17B-GB. There was only one RPD greater than 100 percent for the 10- and 1-percent AEP estimates, whereas 19 RPDs exceeded 100 percent for the 0.2-percent AEP. At streamgaging stations with low-outlier data, historical peak-flow data, or both, RPDs ranged from −84 to 262 percent for the 0.2-percent AEP flow estimate. When streamgaging stations were separated by the presence of historical peak-flow data (that is, no low outliers or censored peaks) or by low outlier peak-flow data (no historical data), the results showed that RPD variability was greatest for the 0.2-AEP flow estimates, indicating that the treatment of historical and (or) low-outlier data was different between methods and that method differences were most influential when estimating the less probable AEP flows (1, 0.5, and 0.2 percent). When regional skew information was weighted with the station skew, B17B-GB estimates were generally higher than the EMA-MGB estimates for any given AEP. This was related to the different regional skews and mean square error used in the weighting procedure for each flood frequency analysis. The B17B-GB weighted skew analysis used a more positive regional skew determined in USGS Water Supply Paper 2433 (Thomas and others, 1997), while the EMA-MGB analysis used a more negative regional skew with a lower mean square error determined from a Bayesian generalized least squares analysis. Regional groupings of streamgaging stations reflected differences in physiographic and climatic characteristics. Potentially influential low flows (PILFs) were more prevalent in arid regions of the State, and generally AEP flows were larger with EMA-MGB than with B17B-GB for gaging stations with PILFs. In most cases EMA-MGB curves would fit the largest floods more accurately than B17B-GB. In areas of the State with more baseflow, such as along the Mogollon Rim and the White Mountains, streamgaging stations generally had fewer PILFs and more positive skews, causing estimated AEP flows to be larger with B17B-GB than with EMA-MGB. The effect of including regional skew was similar for all regions, and the observed pattern was increasingly greater B17B-GB flows (more negative RPDs) with each decreasing AEP quantile. A variation on a goodness-of-fit test statistic was used to describe each method’s ability to fit the largest floods. The mean absolute percent difference between the measured peak flows and the log-Pearson Type 3 (LP3)-estimated flows, for each method, was averaged over the 90th, 75th, and 50th percentiles of peak-flow data at each site. In most percentile subsets, EMA-MGB on average had smaller differences (1 to 3 percent) between the observed and fitted value, suggesting that the EMA-MGB-LP3 distribution is fitting the observed peak-flow data more precisely than B17B-GB. The smallest EMA-MGB percent differences occurred for the greatest 10 percent (90th percentile) of the peak-flow data. When stations were analyzed by USGS NWIS peak flow qualification code groups, the stations with historical peak flows and no low outliers had average percent differences as high as 11 percent greater for B17B-GB, indicating that EMA-MGB utilized the historical information to fit the largest observed floods more accurately. A resampling procedure was used in which 1,000 random subsamples were drawn, each comprising one-half of the observed data. An LP3 distribution was fit to each subsample using B17B-GB and EMA-MGB methods, and the predicted 1-percent AEP flows were compared to those generated from distributions fit to the entire dataset. With station skew, the two methods were similar in the median percent difference, but with weighted skew EMA-MGB estimates were generally better. At two gages where B17B-GB appeared to perform better, a large number of peak flows were deemed to be PILFs by the MGB test, although they did not appear to depart significantly from the trend of the data (step or dogleg appearance). At two gages where EMA-MGB performed better, the MGB identified several PILFs that were affecting the fitted distribution of the B17B-GB method. Monte Carlo simulations were run for the LP3 distribution using different skews and with different assumptions about the expected number of historical peaks. The primary benefit of running Monte Carlo simulations is that the underlying distribution statistics are known, meaning that the true 1-percent AEP is known. The results showed that EMA-MGB performed as well or better in situations where the LP3 distribution had a zero or positive skew and historical information. When the skew for the LP3 distribution was negative, EMA-MGB performed significantly better than B17B-GB and EMA-MGB estimates were less biased by more closely estimating the true 1-percent AEP for 1, 2, and 10 historical flood scenarios.

Arizona↗
USGS2014

Methods for estimating the magnitude and frequency of floods for urban and small, rural streams in Georgia, South Carolina, and North Carolina, 2011

Reliable estimates of the magnitude and frequency of floods are essential for the design of transportation and water-conveyance structures, flood-insurance studies, and flood-plain management. Such estimates are particularly important in densely populated urban areas. In order to increase the number of streamflow-gaging stations (streamgages) available for analysis, expand the geographical coverage that would allow for application of regional regression equations across State boundaries, and build on a previous flood-frequency investigation of rural U.S Geological Survey streamgages in the Southeast United States, a multistate approach was used to update methods for determining the magnitude and frequency of floods in urban and small, rural streams that are not substantially affected by regulation or tidal fluctuations in Georgia, South Carolina, and North Carolina. The at-site flood-frequency analysis of annual peak-flow data for urban and small, rural streams (through September 30, 2011) included 116 urban streamgages and 32 small, rural streamgages, defined in this report as basins draining less than 1 square mile. The regional regression analysis included annual peak-flow data from an additional 338 rural streamgages previously included in U.S. Geological Survey flood-frequency reports and 2 additional rural streamgages in North Carolina that were not included in the previous Southeast rural flood-frequency investigation for a total of 488 streamgages included in the urban and small, rural regression analysis. The at-site flood-frequency analyses for the urban and small, rural streamgages included the expected moments algorithm, which is a modification of the Bulletin 17B log-Pearson type III method for fitting the statistical distribution to the logarithms of the annual peak flows. Where applicable, the flood-frequency analysis also included low-outlier and historic information. Additionally, the application of a generalized Grubbs-Becks test allowed for the detection of multiple potentially influential low outliers. Streamgage basin characteristics were determined using geographical information system techniques. Initial ordinary least squares regression simulations reduced the number of basin characteristics on the basis of such factors as statistical significance, coefficient of determination, Mallow’s Cp statistic, and ease of measurement of the explanatory variable. Application of generalized least squares regression techniques produced final predictive (regression) equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability flows for urban and small, rural ungaged basins for three hydrologic regions (HR1, Piedmont–Ridge and Valley; HR3, Sand Hills; and HR4, Coastal Plain), which previously had been defined from exploratory regression analysis in the Southeast rural flood-frequency investigation. Because of the limited availability of urban streamgages in the Coastal Plain of Georgia, South Carolina, and North Carolina, additional urban streamgages in Florida and New Jersey were used in the regression analysis for this region. Including the urban streamgages in New Jersey allowed for the expansion of the applicability of the predictive equations in the Coastal Plain from 3.5 to 53.5 square miles. Average standard error of prediction for the predictive equations, which is a measure of the average accuracy of the regression equations when predicting flood estimates for ungaged sites, range from 25.0 percent for the 10-percent annual exceedance probability regression equation for the Piedmont–Ridge and Valley region to 73.3 percent for the 0.2-percent annual exceedance probability regression equation for the Sand Hills region.

Georgia, North Carolina, South Carolina↗
USGS2014

Maximum known stages and discharges of New York streams and their annual exceedance probabilities through September 2011

Maximum known stages and discharges at 1,400 sites on 796 streams within New York are tabulated. Stage data are reported in feet. Discharges are reported as cubic feet per second and in cubic feet per second per square mile. Drainage areas range from 0.03 to 298,800 square miles; excluding the three sites with larger drainage areas on the St. Lawrence and Niagara Rivers, which drain the Great Lakes, the maximum drainage area is 8,288 square miles (Hudson River at Albany). Most data were obtained from U.S. Geological Survey (USGS) compilations and records, but some were provided by State, local, and other Federal agencies and by private organizations. The stage and discharge information is grouped by major drainage basins and U.S. Geological Survey site number, in downstream order. Site locations and their associated drainage area, period(s) of record, stage and discharge data, and flood-frequency statistics are compiled in a Microsoft Excel spreadsheet. Flood frequencies were derived for 1,238 sites by using methods described in Bulletin 17B (Interagency Advisory Committee on Water Data, 1982), Ries and Crouse (2002), and Lumia and others (2006). Curves that “envelope” maximum discharges within their range of drainage areas were developed for each of six flood-frequency hydrologic regions and for sites on Long Island, as well as for the State of New York; the New York curve was compared with a curve derived from a plot of maximum known discharges throughout the United States. Discharges represented by the national curve range from at least 2.7 to 4.9 times greater than those represented by the New York curve for drainage areas of 1.0 and 1,000 square miles. The relative magnitudes of discharge and runoff in the six hydrologic regions of New York and Long Island suggest the largest known discharges per square mile are in the southern part of western New York and the Catskill Mountain area, and the smallest are on Long Island.

New York↗
USGS2014

Methods for estimating magnitude and frequency of floods in Arizona, developed with unregulated and rural peak-flow data through water year 2010

Flooding is among the worst natural disasters responsible for loss of life and property in Arizona, underscoring the importance of accurate estimation of flood magnitude for proper structural design and floodplain mapping. Twenty-four years of additional peak-flow data have been recorded since the last comprehensive regional flood frequency analysis conducted in Arizona. Periodically, flood frequency estimates and regional regression equations must be revised to maintain the accurate estimation of flood frequency and magnitude. Annual peak-flow data collected through water year 2010 were compiled from 448 unregulated streamflow-gaging stations, hereafter referred to as streamgages, in Arizona having a minimum of 10 years of record. Flood frequency estimates were first computed with station (or at-site) skew using the Expected Moments Algorithm with a multiple Grubbs-Beck test to identify multiple potentially influential low flows to fit a Pearson Type III distribution. Next, a multiple step Bayesian least-squares-regression approach was used to determine a new statewide regional skew of −0.09. No basin characteristics analyzed were statistically significant in explaining the variation in skew and as a result, the constant model was chosen as the best regional skew model for the Arizona study area. The mean square error used in Bulletin 17B (B17B) of the Interagency Advisory Committee on Water Data is used to describe the precision of the regional skew. The constant model had a mean square error equal to 0.08, which corresponds to an effective record length of 85 years. This is a marked improvement over a previous Arizona regional skew analysis, with a reported mean square error of 0.31, for a corresponding effective record length of around 17 years. Thus the new regional model had almost five times the information content (as measured by effective record length) of that calculated in USGS Water Supply Paper 2433, published in 1997, or the value of 0.302 reported in the B17B generalized skew map. The flood frequency estimates were recalculated using a weighted skew of the station and regional skew. Station flood frequency estimates for each streamgage are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities. Geographical information systems were used to compute basin characteristic information for each streamgage for the purpose of developing regional equations to estimate flood statistics at ungaged basins. Five hydrologic flood regions in Arizona were defined in a multivariate regionalization process based on mean basin elevation, mean annual precipitation, and soil permeability. A regional generalized least-squares-regression analysis was used to develop five sets of equations from 344 nonredundant streamgages, corresponding to five regions, for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities at ungaged basins in Arizona. The regression equations developed for these five regions were based on one or more of the statistically significant explanatory variables: drainage area, mean basin elevation, and mean annual precipitation. Average standard errors of prediction for the regression regions for the five regions ranged from 27 to 122 percent and the pseudo-coefficients of determination (pseudo-R 2 ), a measure of the proportion of peak-flow variation that is explained by the basin characteristics, ranged from 68 to 98 percent. Regression equations for Central Highlands (region 4) had the lowest model error and the greatest pseudo-R 2 metrics. The equations for Colorado Plateau (region 2) regression equations generally had greater model error and lower pseudo-R 2 metrics. The improvement of regional regression equation model error and pseudo-R 2 metrics was related to higher numbers of streamgages, longer period of record, and even spatial coverage within a region. The regional regression equations were integrated into the U.S. Geological Survey’s StreamStats program. The StreamStats program is a national map-based web application that allows the public to easily access published flood frequency and basin characteristic statistics. The interactive web application allows a user to select a point within a watershed (gaged or ungaged) and retrieve flood-frequency estimates derived from the current regional regression equations and geographic information system data within the selected basin. StreamStats provides users with an efficient and accurate means for retrieving the most up to date flood frequency and basin characteristic data. StreamStats is intended to provide consistent statistics, minimize user error, and reduce the need for large datasets and costly geographic information system software.

Arizona↗
USGS2017

Application of at-site peak-streamflow frequency analyses for very low annual exceedance probabilities

The U.S. Geological Survey (USGS), in cooperation with the U.S. Nuclear Regulatory Commission, has investigated statistical methods for probabilistic flood hazard assessment to provide guidance on very low annual exceedance probability (AEP) estimation of peak-streamflow frequency and the quantification of corresponding uncertainties using streamgage-specific data. The term “very low AEP” implies exceptionally rare events defined as those having AEPs less than about 0.001 (or 1 × 10 –3 in scientific notation or for brevity 10 –3 ). Such low AEPs are of great interest to those involved with peak-streamflow frequency analyses for critical infrastructure, such as nuclear power plants. Flood frequency analyses at streamgages are most commonly based on annual instantaneous peak streamflow data and a probability distribution fit to these data. The fitted distribution provides a means to extrapolate to very low AEPs. Within the United States, the Pearson type III probability distribution, when fit to the base-10 logarithms of streamflow, is widely used, but other distribution choices exist. The USGS-PeakFQ software, implementing the Pearson type III within the Federal agency guidelines of Bulletin 17B (method of moments) and updates to the expected moments algorithm (EMA), was specially adapted for an “Extended Output” user option to provide estimates at selected AEPs from 10 –3 to 10 –6 . Parameter estimation methods, in addition to product moments and EMA, include L-moments, maximum likelihood, and maximum product of spacings (maximum spacing estimation). This study comprehensively investigates multiple distributions and parameter estimation methods for two USGS streamgages (01400500 Raritan River at Manville, New Jersey, and 01638500 Potomac River at Point of Rocks, Maryland). The results of this study specifically involve the four methods for parameter estimation and up to nine probability distributions, including the generalized extreme value, generalized log-normal, generalized Pareto, and Weibull. Uncertainties in streamflow estimates for corresponding AEP are depicted and quantified as two primary forms: quantile (aleatoric [random sampling] uncertainty) and distribution-choice (epistemic [model] uncertainty). Sampling uncertainties of a given distribution are relatively straightforward to compute from analytical or Monte Carlo-based approaches. Distribution-choice uncertainty stems from choices of potentially applicable probability distributions for which divergence among the choices increases as AEP decreases. Conventional goodness-of-fit statistics, such as Cramér-von Mises, and L-moment ratio diagrams are demonstrated in order to hone distribution choice. The results generally show that distribution choice uncertainty is larger than sampling uncertainty for very low AEP values.

Scientific Investigations Report↗
USGS2021

Improving flood-frequency analysis with a 4,000-year record of flooding on the Tennessee River near Chattanooga, Tennessee

This comprehensive field study applied paleoflood hydrology methods to estimate the frequency of low-probability floods for the Tennessee River near Chattanooga, Tennessee. The study combined stratigraphic records of large, previously unrecorded floods with modern streamflow records and historical flood accounts. The overall approach was to (1) develop a flood chronology for the Tennessee River near Chattanooga using stratigraphic analyses and geochronology from multiple sites at multiple elevations in the study area; (2) estimate peak flow magnitudes associated with elevations of flood evidence using a one-dimensional hydraulic model; (3) combine the information obtained from steps 1 and 2 to develop a history of timing and magnitude of large floods in the study reach; and (4) use all available information (including paleoflood, gaged, and historical records of flooding) to estimate flood frequency using a standardized statistical approach for flood-frequency analysis. The stratigraphy, geochronology, and hydraulic modeling results from all paleoflood sites along the Tennessee River were distilled into an overall chronology of the number, timing, and magnitude of large unrecorded floods. In total, 30 sites were identified and the stratigraphy of 17 of those sites was closely examined, measured, and recorded. Flood-frequency analyses were done using the U.S. Geological Survey software program PeakFQ v7.2 that follows the Guidelines for Determining Flood Flow Frequency—Bulletin 17C. Resolving stratigraphic and chronologic information from all 17 sites yielded information for eight unique large floods in the last 3,500–4,000 years for the Tennessee River near Chattanooga. Two of these floods had discharges of 470,000 cubic feet per second (ft 3 /s), slightly greater than the 1867 historical peak at the Chattanooga streamgage (459,000 ft 3 /s). One flood with a discharge of 1,100,000 ft 3 /s was substantially greater than any other flood on the Tennessee River during the last several thousand years. This large flood occurred only a few hundred years ago, likely in the mid-to-late 1600s. Two additional floods in the last 1,000 years had estimated magnitudes of about 420,000 and 400,000 ft 3 /s. The remaining three unique floods identified in the paleoflood record were much smaller (less than 240,000 ft 3 /s) and occurred about 3,000–800 years ago. Flood-frequency analyses show that the addition of paleoflood information markedly improves estimates of low probability floods—most clearly shown by substantial narrowing of the 95-percent confidence limits. For the most plausible flood scenario, the 95-percent confidence interval for the 1,000-year quantile estimate derived from incorporating the four most recent paleofloods is about 480,000–620,000 ft 3 /s compared to about 380,000–610,000 ft 3 /s for the gaged and historical record alone, a reduction in the uncertainty of the estimate by 38 percent. Similarly, uncertainty for all flood quantile estimates from 100 to 10,000 years was reduced by 22–44 percent by the addition of the paleoflood record to the flood-frequency analyses.

Tennessee↗
USGS2021

Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017

The methods of computation and estimates of the magnitude of flood flows were updated for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance levels for 91 streamgages on the main island of Puerto Rico by using annual peak-flow data through 2017. Since the previous flood frequency study in 1994, the U.S. Geological Survey has collected additional peak flows at additional streamgages, and Puerto Rico has experienced numerous flood events. This updated study was performed using longer annual peak-flow datasets from more stations to provide more representative equations to predict flood flows. Screening criteria for these streamgages included 10 or more years of annual peak-flow data, unregulated flow, and less than 10 percent impervious drainage area. The magnitude and frequency of floods at selected streamgages in Puerto Rico were estimated using updated methods outlined in Bulletin 17C. The new procedures include a regional skew analysis that incorporates Bayesian regression techniques, the Expected Moments Algorithm to better represent missing record and estimate parameters of the log-Pearson Type III distribution, and the Multiple Grubbs-Beck test for low outlier detection. Regional regression equations were developed to estimate peak-flow statistics at ungaged locations by using selected basin and climatic characteristics as explanatory variables. These variables were determined from digital spatial datasets and geographic information systems by using the most recent data available. Ordinary least-squares regression techniques were used to filter the basin characteristics and determine two separate regions, region 1 (west) and region 2 (east), based on residuals. A generalized least-squares procedure was used to account for cross-correlation of sites and develop the final set of equations that have drainage area as the only explanatory variable. The average standard errors of prediction ranged from 18.7 to 46.7 percent in region 1 and 33.4 to 57.6 percent in region 2 for all annual exceedance probabilities (AEPs) examined. The updated statistics showed a greater accuracy of prediction when compared to those from the previous study using drainage area as the only explanatory variable for all AEPs examined in region 1 and the 0.01 and 0.002 AEP flows for region 2. When compared to equations developed in the previous study that have drainage area, mean annual rainfall, and (or) depth-to-rock as explanatory variables, the updated statistics show a greater accuracy of prediction in region 1 at AEP flows of 0.02 and lower (that is, higher flows). Those developed for region 2 do not show a greater accuracy of prediction for any AEP flows when compared to the equations having multiple explanatory variables in the previous study. The calculated regression equations, basin characteristics, and at-site statistics will be incorporated into the U.S. Geological Survey web application, StreamStats ( https://streamstats.usgs.gov/ss/ ). This application allows users to select a location on a stream, whether gaged or ungaged, to obtain estimates of basin characteristics and flow statistics.

Puerto Rico↗