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At least 19 recordsLinked to original sources
USGS1993

An objective replacement method for censored geochemical data

Geochemical data are commonly censored, that is, concentrations for some samples are reported as "less than" or "greater than" some value. Censored data hampers statistical analysis because certain computational techniques used in statistical analysis require a complete set of uncensored data. We show that the simple substitution method for creating an uncensored dataset, e.g., replacement by 3/4 times the detection limit, has serious flaws, and we present an objective method to determine the replacement value. Our basic premise is that the replacement value should equal the mean of the actual values represented by the qualified data. We adapt the maximum likelihood approach (Cohen, 1961) to estimate this mean. This method reproduces the mean and skewness as well or better than a simple substitution method using 3/4 of the lower detection limit or 3/4 of the upper detection limit. For a small proportion of "less than" substitutions, a simple-substitution replacement factor of 0.55 is preferable to 3/4; for a small proportion of "greater than" substitutions, a simple-substitution replacement factor of 1.7 is preferable to 4/3, provided the resulting replacement value does not exceed 100%. For more than 10% replacement, a mean empirical factor may be used. However, empirically determined simple-substitution replacement factors usually vary among different data sets and are less reliable with more replacements. Therefore, a maximum likelihood method is superior in general. Theoretical and empirical analyses show that true replacement factors for "less thans" decrease in magnitude with more replacements and larger standard deviation; those for "greater thans" increase in magnitude with more replacements and larger standard deviation. In contrast to any simple substitution method, the maximum likelihood method reproduces these variations. Using the maximum likelihood method for replacing "less thans" in our sample data set, correlation coefficients were reasonably accurately estimated in 90% of the cases for as much as 40% replacement and in 60% of the cases for 80% replacement. These results suggest that censored data can be utilized more than is commonly realized. ?? 1993 International Association for Mathematical Geology.

Mathematical Geology↗
USGS2014

Parameter estimation for the 4-parameter Asymmetric Exponential Power distribution by the method of L-moments using R

The implementation characteristics of two method of L-moments (MLM) algorithms for parameter estimation of the 4-parameter Asymmetric Exponential Power (AEP4) distribution are studied using the R environment for statistical computing. The objective is to validate the algorithms for general application of the AEP4 using R. An algorithm was introduced in the original study of the L-moments for the AEP4. A second or alternative algorithm is shown to have a larger L-moment-parameter domain than the original. The alternative algorithm is shown to provide reliable parameter production and recovery of L-moments from fitted parameters. A proposal is made for AEP4 implementation in conjunction with the 4-parameter Kappa distribution to create a mixed-distribution framework encompassing the joint L-skew and L-kurtosis domains. The example application provides a demonstration of pertinent algorithms with L-moment statistics and two 4-parameter distributions (AEP4 and the Generalized Lambda) for MLM fitting to a modestly asymmetric and heavy-tailed dataset using R.

Computational Statistics and Data Analysis↗
USGS1973

Magnitude and frequency of floods in small drainage basins in Idaho

A method is presented in this report for determining magnitude and frequency of floods on streams with drainage areas between 0.5 and 200 square miles. The method relates basin characteristics, including drainage area, percentage of forest cover, percentage of water area, latitude, and longitude, with peak flow characteristics. Regression equations for each of eight regions are presented for determination of QIQ/ the peak discharge, which, on the average, will be exceeded once in 10 years. Peak flows, Q25 and Q 50 , can then be estimated from Q25/Q10 and Q-50/Q-10 ratios developed for each region. Nomographs are included which solve the equations for basins between 1 and 50 square miles. The regional regression equations were developed using multiple regression techniques. Annual peaks for 303 sites were analyzed in the study. These included all records on unregulated streams with drainage areas less than about 500 square miles with 10 years or more of record or which could readily be extended to 10 years on the basis of nearby streams. The log-Pearson Type III method as modified and a digital computer were employed to estimate magnitude and frequency of floods for each of the 303 gaged sites. A large number of physical and climatic basin characteristics were determined for each of the gaged sites. The multiple regression method was then applied to determine the equations relating the floodflows and the most significant basin characteristics. For convenience of the users, several equations were simplified and some complex characteristics were deleted at the sacrifice of some increase in the standard error. Standard errors of estimate and many other statistical data were computed in the analysis process and are available in the Boise district office files. The analysis showed that QIQ was the best defined and most practical index flood for determination of the Q25 and 0,50 flood estimates. Regression equations are not developed because of poor definition for areas which total about 20,000 square miles, most of which are in southern Idaho. These areas are described in the report to prevent use of regression equations where they do not apply. They include urbanized areas, streams affected by regulation or diversion by works of man, unforested areas, streams with gaining or losing reaches, streams draining alluvial valleys and the Snake Plain, intense thunderstorm areas, and scattered areas where records indicate recurring floods which depart from the regional equations. Maximum flows of record and basin locations are summarized in tables and maps. The analysis indicates deficiencies in data exist. To improve knowledge regarding flood characteristics in poorly defined areas, the following data-collection programs are recommended. Gages should be operated on a few selected small streams for an extended period to define floods at long recurrence intervals. Crest-stage gages should be operated in representative basins in urbanized areas, newly developed irrigated areas and grasslands, and in unforested areas. Unusual floods should continue to be measured at miscellaneous sites on regulated streams and in intense thunderstorm-prone areas. The relationship between channel geometry and floodflow characteristics should be investigated as an alternative or supplement to operation of gaging stations. Documentation of historic flood data from newspapers and other sources would improve the basic flood-data base.

Idaho↗
USGS2009

Computer programs for obtaining and analyzing daily mean streamflow data from the U.S. Geological Survey National Water Information System web site

Five computer programs were developed for obtaining and analyzing streamflow from the National Water Information System (NWISWeb). The programs were developed as part of a study by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, to develop a stochastic empirical loading and dilution model. The programs were developed because reliable, efficient, and repeatable methods are needed to access and process streamflow information and data. The first program is designed to facilitate the downloading and reformatting of NWISWeb streamflow data. The second program is designed to facilitate graphical analysis of streamflow data. The third program is designed to facilitate streamflow-record extension and augmentation to help develop long-term statistical estimates for sites with limited data. The fourth program is designed to facilitate statistical analysis of streamflow data. The fifth program is a preprocessor to create batch input files for the U.S. Environmental Protection Agency DFLOW3 program for calculating low-flow statistics. These computer programs were developed to facilitate the analysis of daily mean streamflow data for planning-level water-quality analyses but also are useful for many other applications pertaining to streamflow data and statistics. These programs and the associated documentation are included on the CD-ROM accompanying this report. This report and the appendixes on the CD-ROM describe the implementation and use of the programs and the interpretation of results from the programs. The body of this report provides an overview of the five programs included on this CD-ROM. The appendixes are the software manuals for each program. These manuals describe statistical and numerical methods used to implement each program, input-file formats, output-file formats, installation of the programs, and use of the programs. Each appendix is written as a self-contained manual because each program may have many uses alone or in tandem with other programs on the CD-ROM. Each of these programs uses graphical user interface that follows standard Microsoft Windows interface conventions.

Open-File Report↗
USGS2007

An initial-abstraction, constant-loss model for unit hydrograph modeling for applicable watersheds in Texas

Estimation of representative hydrographs from design storms, which are known as design hydrographs, provides for cost-effective, riskmitigated design of drainage structures such as bridges, culverts, roadways, and other infrastructure. During 2001?07, the U.S. Geological Survey (USGS), in cooperation with the Texas Department of Transportation, investigated runoff hydrographs, design storms, unit hydrographs,and watershed-loss models to enhance design hydrograph estimation in Texas. Design hydrographs ideally should mimic the general volume, peak, and shape of observed runoff hydrographs. Design hydrographs commonly are estimated in part by unit hydrographs. A unit hydrograph is defined as the runoff hydrograph that results from a unit pulse of excess rainfall uniformly distributed over the watershed at a constant rate for a specific duration. A time-distributed, watershed-loss model is required for modeling by unit hydrographs. This report develops a specific time-distributed, watershed-loss model known as an initial-abstraction, constant-loss model. For this watershed-loss model, a watershed is conceptualized to have the capacity to store or abstract an absolute depth of rainfall at and near the beginning of a storm. Depths of total rainfall less than this initial abstraction do not produce runoff. The watershed also is conceptualized to have the capacity to remove rainfall at a constant rate (loss) after the initial abstraction is satisfied. Additional rainfall inputs after the initial abstraction is satisfied contribute to runoff if the rainfall rate (intensity) is larger than the constant loss. The initial abstraction, constant-loss model thus is a two-parameter model. The initial-abstraction, constant-loss model is investigated through detailed computational and statistical analysis of observed rainfall and runoff data for 92 USGS streamflow-gaging stations (watersheds) in Texas with contributing drainage areas from 0.26 to 166 square miles. The analysis is limited to a previously described, watershed-specific, gamma distribution model of the unit hydrograph. In particular, the initial-abstraction, constant-loss model is tuned to the gamma distribution model of the unit hydrograph. A complex computational analysis of observed rainfall and runoff for the 92 watersheds was done to determine, by storm, optimal values of initial abstraction and constant loss. Optimal parameter values for a given storm were defined as those values that produced a modeled runoff hydrograph with volume equal to the observed runoff hydrograph and also minimized the residual sum of squares of the two hydrographs. Subsequently, the means of the optimal parameters were computed on a watershed-specific basis. These means for each watershed are considered the most representative, are tabulated, and are used in further statistical analyses. Statistical analyses of watershed-specific, initial abstraction and constant loss include documentation of the distribution of each parameter using the generalized lambda distribution. The analyses show that watershed development has substantial influence on initial abstraction and limited influence on constant loss. The means and medians of the 92 watershed-specific parameters are tabulated with respect to watershed development; although they have considerable uncertainty, these parameters can be used for parameter prediction for ungaged watersheds. The statistical analyses of watershed-specific, initial abstraction and constant loss also include development of predictive procedures for estimation of each parameter for ungaged watersheds. Both regression equations and regression trees for estimation of initial abstraction and constant loss are provided. The watershed characteristics included in the regression analyses are (1) main-channel length, (2) a binary factor representing watershed development, (3) a binary factor representing watersheds with an abundance of rocky and thin-soiled terrain, and (4) curve numb

Scientific Investigations Report↗
USGS1974

Gravel resources, urbanization, and future land use, Front Range Urban Corridor, Colorado

An assessment of gravel needs in Front Range Urban Corridor markets to 2000 A.D., based on forecast population increases and urbanization, indicates that adequate resources to meet anticipated needs are potentially available, if future land use does not preclude their extraction. Because of urban encroachment onto gravel-bearing lands, this basic construction material is in short supply nationally and in the Front Range Urban Corridor. Longer hauls, increased prices, and use of alternatives, especially crushed rock aggregate, have resulted. An analysis of possible sequential land uses following gravel mining indicates that a desirable use is for 'real estate' ponds and small lakes. A method for computing gravel reserves, based on planimeter measurement of area of resource-bearing lands and statistical analysis of reliability of thickness and size distribution data, was developed to compute reserves in individual markets. A discussion of the qualitative 'usability' of these reserves is then made for the individual markets.

Colorado↗
USGS2004

Computing and software

The reality is that the statistical methods used for analysis of data depend upon the availability of software. Analysis of marked animal data is no different than the rest of the statistical field. The methods used for analysis are those that are available in reliable software packages. Thus, the critical importance of having reliable, up–to–date software available to biologists is obvious. Statisticians have continued to develop more robust models, ever expanding the suite of potential analysis methods available. But without software to implement these newer methods, they will languish in the abstract, and not be applied to the problems deserving them. In the Computers and Software Session, two new software packages are described, a comparison of implementation of methods for the estimation of nest survival is provided, and a more speculative paper about how the next generation of software might be structured is presented. Rotella et al. (2004) compare nest survival estimation with different software packages: SAS logistic regression, SAS non–linear mixed models, and Program MARK. Nests are assumed to be visited at various, possibly infrequent, intervals. All of the approaches described compute nest survival with the same likelihood, and require that the age of the nest is known to account for nests that eventually hatch. However, each approach offers advantages and disadvantages, explored by Rotella et al. (2004). Efford et al. (2004) present a new software package called DENSITY. The package computes population abundance and density from trapping arrays and other detection methods with a new and unique approach. DENSITY represents the first major addition to the analysis of trapping arrays in 20 years. Barker & White (2004) discuss how existing software such as Program MARK require that each new model’s likelihood must be programmed specifically for that model. They wishfully think that future software might allow the user to combine pieces of likelihood functions together to generate estimates. The idea is interesting, and maybe some bright young statistician can work out the specifics to implement the procedure. Choquet et al. (2004) describe MSURGE, a software package that implements the multistate capture–recapture models. The unique feature of MSURGE is that the design matrix is constructed with an interpreted language called GEMACO. Because MSURGE is limited to just multistate models, the special requirements of these likelihoods can be provided. The software and methods presented in these papers gives biologists and wildlife managers an expanding range of possibilities for data analysis. Although ease–of–use is generally getting better, it does not replace the need for understanding of the requirements and structure of the models being computed. The internet provides access to many free software packages as well as user–discussion groups to share knowledge and ideas. (A starting point for wildlife–related applications is (http://www.phidot.org).

Animal Biodiversity and Conservation↗
USGS2003

Methods for quality-assurance review of water-quality data in New Jersey

This report is an instructional and reference manual that describes methods developed and used by the U.S. Geological Survey (USGS), New Jersey District, to assure the accuracy and precision of the results of analyses of surface- and ground-water samples received from analyzing laboratories and, ultimately, to ensure the integrity of water-quality data in USGS databases and published reports. A statistical-analysis computer program, COMP.PPL, is used to determine whether the values reported by the laboratories are internally consistent, whether they are reasonable when compared with values for samples previously collected at the same site, and whether they exceed applicable drinking-water regulations. The program output consists of three files -- QWREVIEW, QWOUTLIERS, and QWCALC. QWREVIEW presents the results of tests of chemical logic and shows values that exceed drinking-water regulations. QWOUTLIERS identifies values that fall outside the historical range of values for the site sampled. QWCALC shows values and calculations used for reference purposes. Because values that are identified by the program as questionable may or may not be in error, the reviewer looks at both qualitative and quantitative relations between analytes during the period of record and then uses technical judgement to decide whether to accept a questionable value or investigate further. Guidelines for, and the use of regression analysis in, making this decision are described. Instructions are given for requesting that the analyzing laboratory reanalyze a constituent or otherwise verify the reported value. If, upon reanalysis or verification, a value is still questionable, consideration must be given to deleting the value or marking the value in the USGS National Water Information System database as having been reviewed and rejected.

New Jersey↗
USGS1977

The U.S. geological survey rass-statpac system for management and statistical reduction of geochemical data

RASS is an acronym for Rock Analysis Storage System and STATPAC, for Statistical Package. The RASS and STATPAC computer programs are integrated into the RASS-STATPAC system for the management and statistical reduction of geochemical data. The system, in its present form, has been in use for more than 9 yr by scores of U.S. Geological Survey geologists, geochemists, and other scientists engaged in a broad range of geologic and geochemical investigations. The principal advantage of the system is the flexibility afforded the user both in data searches and retrievals and in the manner of statistical treatment of data. The statistical programs provide for most types of statistical reduction normally used in geochemistry and petrology, but also contain bridges to other program systems for statistical processing and automatic plotting. ?? 1977.

Computers & Geosciences↗
USGS2013

The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data

The movement of animals is strongly influenced by external factors in their surrounding environment such as weather, habitat types, and human land use. With advances in positioning and sensor technologies, it is now possible to capture animal locations at high spatial and temporal granularities. Likewise, scientists have an increasing access to large volumes of environmental data. Environmental data are heterogeneous in source and format, and are usually obtained at different spatiotemporal scales than movement data. Indeed, there remain scientific and technical challenges in developing linkages between the growing collections of animal movement data and the large repositories of heterogeneous remote sensing observations, as well as in the developments of new statistical and computational methods for the analysis of movement in its environmental context. These challenges include retrieval, indexing, efficient storage, data integration, and analytical techniques.

Movement Ecology↗
USGS2009

Magnitude and Frequency of Rural Floods in the Southeastern United States, through 2006: Volume 2, North Carolina

Reliable estimates of the magnitude and frequency of floods are required for the economical and safe design of transportation and water-conveyance structures. A multistate approach was used to update methods for estimating the magnitude and frequency of floods in rural, ungaged basins in North Carolina, South Carolina, and Georgia that are not substantially affected by regulation, tidal fluctuations, or urban development. In North Carolina, annual peak-flow data available through September 2006 were available for 584 sites; 402 of these sites had a total of 10 or more years of systematic record that is required for at-site, flood-frequency analysis. Following data reviews and the computation of 20 physical and climatic basin characteristics for each station as well as at-site flood-frequency statistics, annual peak-flow data were identified for 363 sites in North Carolina suitable for use in this analysis. Among these 363 sites, 19 sites had records that could be divided into unregulated and regulated/ channelized annual peak discharges, which means peak-flow records were identified for a total of 382 cases in North Carolina. Considering the 382 cases, at-site flood-frequency statistics are provided for 333 unregulated cases (also used for the regression database) and 49 regulated/channelized cases. The flood-frequency statistics for the 333 unregulated sites were combined with data for sites from South Carolina, Georgia, and adjacent parts of Alabama, Florida, Tennessee, and Virginia to create a database of 943 sites considered for use in the regional regression analysis. Flood-frequency statistics were computed by fitting logarithms (base 10) of the annual peak flows to a log-Pearson Type III distribution. As part of the computation process, a new generalized skew coefficient was developed by using a Bayesian generalized least-squares regression model. Exploratory regression analyses using ordinary least-squares regression completed on the initial database of 943 sites resulted in defining five hydrologic regions for North Carolina, South Carolina, and Georgia. Stations with drainage areas less than 1 square mile were removed from the database, and a procedure to examine for basin redundancy (based on drainage area and periods of record) also resulted in the removal of some stations from the regression database. Flood-frequency estimates and basin characteristics for 828 gaged stations were combined to form the final database that was used in the regional regression analysis. Regional regression analysis, using generalized least-squares regression, was used to develop a set of predictive equations that can be used for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance flows for rural ungaged, basins in North Carolina, South Carolina, and Georgia. The final predictive equations are all functions of drainage area and the percentage of drainage basin within each of the five hydrologic regions. Average errors of prediction for these regression equations range from 34.0 to 47.7 percent. Discharge estimates determined from the systematic records for the current study are, on average, larger in magnitude than those from a previous study for the highest percent chance exceedances (50 and 20 percent) and tend to be smaller than those from the previous study for the lower percent chance exceedances when all sites are considered as a group. For example, mean differences for sites in the Piedmont hydrologic region range from positive 0.5 percent for the 50-percent chance exceedance flow to negative 4.6 percent for the 0.2-percent chance exceedance flow when stations are grouped by hydrologic region. Similarly for the same hydrologic region, median differences range from positive 0.9 percent for the 50-percent chance exceedance flow to negative 7.1 percent for the 0.2-percent chance exceedance flow. However, mean and median percentage differences between the estimates from the previous and curre

North Carolina↗
USGS2015

Spatially explicit spectral analysis of point clouds and geospatial data

The increasing use of spatially explicit analyses of high-resolution spatially distributed data (imagery and point clouds) for the purposes of characterising spatial heterogeneity in geophysical phenomena necessitates the development of custom analytical and computational tools. In recent years, such analyses have become the basis of, for example, automated texture characterisation and segmentation, roughness and grain size calculation, and feature detection and classification, from a variety of data types. In this work, much use has been made of statistical descriptors of localised spatial variations in amplitude variance (roughness), however the horizontal scale (wavelength) and spacing of roughness elements is rarely considered. This is despite the fact that the ratio of characteristic vertical to horizontal scales is not constant and can yield important information about physical scaling relationships. Spectral analysis is a hitherto under-utilised but powerful means to acquire statistical information about relevant amplitude and wavelength scales, simultaneously and with computational efficiency. Further, quantifying spatially distributed data in the frequency domain lends itself to the development of stochastic models for probing the underlying mechanisms which govern the spatial distribution of geological and geophysical phenomena. The software package PySESA (Python program for Spatially Explicit Spectral Analysis) has been developed for generic analyses of spatially distributed data in both the spatial and frequency domains. Developed predominantly in Python , it accesses libraries written in Cython and C++ for efficiency. It is open source and modular, therefore readily incorporated into, and combined with, other data analysis tools and frameworks with particular utility for supporting research in the fields of geomorphology, geophysics, hydrography, photogrammetry and remote sensing. The analytical and computational structure of the toolbox is described, and its functionality illustrated with an example of a high-resolution bathymetric point cloud data collected with multibeam echosounder.

Computers & Geosciences↗
USGS2010

Development and analysis of a meteorological database, Argonne National Laboratory, Illinois

A database of hourly values of air temperature, dewpoint temperature, wind speed, and solar radiation from January 1, 1948, to September 30, 2003, primarily using data collected at the Argonne National Laboratory station, was developed for use in continuous-time hydrologic modeling in northeastern Illinois. Missing and apparently erroneous data values were replaced with adjusted values from nearby stations used as 'backup'. Temporal variations in the statistical properties of the data resulting from changes in measurement and data-storage methodologies were adjusted to match the statistical properties resulting from the data-collection procedures that have been in place since January 1, 1989. The adjustments were computed based on the regressions between the primary data series from Argonne National Laboratory and the backup series using data obtained during common periods; the statistical properties of the regressions were used to assign estimated standard errors to values that were adjusted or filled from other series. Each hourly value was assigned a corresponding data-source flag that indicates the source of the value and its transformations. An analysis of the data-source flags indicates that all the series in the database except dewpoint have a similar fraction of Argonne National Laboratory data, with about 89 percent for the entire period, about 86 percent from 1949 through 1988, and about 98 percent from 1989 through 2003. The dewpoint series, for which observations at Argonne National Laboratory did not begin until 1958, has only about 71 percent Argonne National Laboratory data for the entire period, about 63 percent from 1948 through 1988, and about 93 percent from 1989 through 2003, indicating a lower reliability of the dewpoint sensor. A basic statistical analysis of the filled and adjusted data series in the database, and a series of potential evapotranspiration computed from them using the computer program LXPET (Lamoreux Potential Evapotranspiration) also was carried out. This analysis indicates annual cycles in solar radiation and potential evapotranspiration that follow the annual cycle of extraterrestrial solar radiation, whereas temperature and dewpoint annual cycles are lagged by about 1 month relative to the solar cycle. The annual cycle of wind has a late summer minimum, and spring and fall maximums. At the annual time scale, the filled and adjusted data series and computed potential evapotranspiration have significant serial correlation and possibly have significant temporal trends. The inter-annual fluctuations of temperature and dewpoint are weakest, whereas those of wind and potential evapotranspiration are strongest.

Open-File Report↗
USGS2007

Surface-source downhole seismic analysis in R

This report discusses a method for interpreting a layered slowness or velocity model from surface-source downhole seismic data originally presented by Boore (2003). I have implemented this method in the statistical computing language R (R Development Core Team, 2007), so that it is freely and easily available to researchers and practitioners that may find it useful. I originally applied an early version of these routines to seismic cone penetration test data (SCPT) to analyze the horizontal variability of shear-wave velocity within the sediments in the San Francisco Bay area (Thompson et al., 2006). A more recent version of these codes was used to analyze the influence of interface-selection and model assumptions on velocity/slowness estimates and the resulting differences in site amplification (Boore and Thompson, 2007). The R environment has many benefits for scientific and statistical computation; I have chosen R to disseminate these routines because it is versatile enough to program specialized routines, is highly interactive which aids in the analysis of data, and is freely and conveniently available to install on a wide variety of computer platforms. These scripts are useful for the interpretation of layered velocity models from surface-source downhole seismic data such as deep boreholes and SCPT data. The inputs are the travel-time data and the offset of the source at the surface. The travel-time arrivals for the P- and S-waves must already be picked from the original data. An option in the inversion is to include estimates of the standard deviation of the travel-time picks for a weighted inversion of the velocity profile. The standard deviation of each travel-time pick is defined relative to the standard deviation of the best pick in a profile and is based on the accuracy with which the travel-time measurement could be determined from the seismogram. The analysis of the travel-time data consists of two parts: the identification of layer-interfaces, and the inversion for the velocity of each layer. The analyst usually picks layer-interfaces by visual inspection of the travel-time data. I have also developed an algorithm that automatically finds boundaries which can save a significant amount of the time when analyzing a large number of sites. The results of the automatic routines should be reviewed to check that they are reasonable. The interactivity of these scripts allows the user to add and to remove layers quickly, thus allowing rapid feedback on how the residuals are affected by each additional parameter in the inversion. In addition, the script allows many models to be compared at the same time.

Open-File Report↗
USGS2018

Implementation of MOVE.1, censored MOVE.1, and piecewise MOVE.1 low-flow regressions with applications at partial-record streamgaging stations in New Jersey

The U.S. Geological Survey (USGS) uses Maintenance of Variance Extension Type 1 (MOVE.1) regression to transfer streamflows measured at long-term continuous-record streamgaging stations to partial-record (PR) streamgaging stations where intermittent base-flow measurements are available. MOVE.1 regression is used widely throughout the hydrologic community to extend historic low flows and low-flow statistics at continuous-record streamgaging stations to streamgaging stations that have access to only a partial record of low flows. The method correlates base-flow measurements at PR streamgaging stations with daily mean streamflows measured at index stations that exhibit similar streamflow characteristics. Following changes in the computing platform for storing, processing, retrieving, and publishing National Water Information System (NWIS) hydrologic data, legacy Statistical Analysis System (SAS) code developed by the USGS to implement the MOVE.1 regression was no longer suitable for reading and processing NWIS streamflow data. To migrate the MOVE.1 program so that it could continue to read streamflow data using the new hydrologic data platform, the SAS code was re-written in R, an open source programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The work described in this report was performed in a study conducted by USGS in cooperation with the New Jersey Department of Environmental Protection. During migration from SAS to R, graphical and tabular output generated by the R script was compared to output produced by the legacy SAS code to ensure that equations used to perform the MOVE.1 regression remained the same. An option to perform censored MOVE.1 regression was added to extend the MOVE.1 methodology to cases where one or more measured continuous-record or PR streamgaging station flows are zero valued. In addition to permitting censored regression, the new R script includes an option to perform piecewise MOVE.1 regression when the relation between PR station and index station low flows varies significantly across the range of index station streamflows. Together with traditional MOVE.1 regression, censored, and piecewise MOVE.1 regression methods implemented by the R script offer less biased estimates than ordinary least squares regression for the annual 7-day 10-year and other low-flow statistics at PR stations for a range of base-flow conditions. The R script is used to implement the MOVE.1 regression methods across a variety of computing platforms.

New Jersey↗
USGS2002

Plotting equation for Gaussian percentiles and a spreadsheet program for generating probability plots

Two-dimensional plotting tools can be of invaluable assistance in analytical scientific pursuits, and have been widely used in the analysis and interpretation of sedimentologic data. We consider, in this work, the use of arithmetic probability paper (APP). Most statistical computer applications do not allow for the generation of APP plots, because of apparent intractable nonlinearity of the percentile (or probability) axis of the plot. We have solved this problem by identifying an equation(s) for determining plotting positions of Gaussian percentiles (or probabilities), so that APP plots can easily be computer generated. An EXCEL example is presented, and a programmed, simple-to-use EXCEL application template is hereby made publicly available, whereby a complete granulometric analysis including data listing, moment measure calculations, and frequency and cumulative APP plots, is automatically produced.

Journal of Sedimentary Research↗
USGS2007

A global search inversion for earthquake kinematic rupture history: Application to the 2000 western Tottori, Japan earthquake

[1] We present a two-stage nonlinear technique to invert strong motions records and geodetic data to retrieve the rupture history of an earthquake on a finite fault. To account for the actual rupture complexity, the fault parameters are spatially variable peak slip velocity, slip direction, rupture time and risetime. The unknown parameters are given at the nodes of the subfaults, whereas the parameters within a subfault are allowed to vary through a bilinear interpolation of the nodal values. The forward modeling is performed with a discrete wave number technique, whose Green's functions include the complete response of the vertically varying Earth structure. During the first stage, an algorithm based on the heat-bath simulated annealing generates an ensemble of models that efficiently sample the good data-fitting regions of parameter space. In the second stage (appraisal), the algorithm performs a statistical analysis of the model ensemble and computes a weighted mean model and its standard deviation. This technique, rather than simply looking at the best model, extracts the most stable features of the earthquake rupture that are consistent with the data and gives an estimate of the variability of each model parameter. We present some synthetic tests to show the effectiveness of the method and its robustness to uncertainty of the adopted crustal model. Finally, we apply this inverse technique to the well recorded 2000 western Tottori, Japan, earthquake ( Mw 6.6); we confirm that the rupture process is characterized by large slip (3-4 m) at very shallow depths but, differently from previous studies, we imaged a new slip patch (2-2.5 m) located deeper, between 14 and 18 km depth.

Tottori↗
USGS1982

Data management system for USGS/USEPA urban hydrology studies program

A data management system was developed to store, update, and retrieve data collected in urban stormwater studies jointly conducted by the U.S. Geological Survey and U.S. Environmental Protection Agency in 11 cities in the United States. The data management system is used to retrieve and combine data from USGS data files for use in rainfall, runoff, and water-quality models and for data computations such as storm loads. The system is based on the data management aspect of the Statistical Analysis System (SAS) and was used to create all the data files in the data base. SAS is used for storage and retrieval of basin physiography, land-use, and environmental practices inventory data. Also, storm-event water-quality characteristics are stored in the data base. The advantages of using SAS to create and manage a data base are many with a few being that it is simple, easy to use, contains a comprehensive statistical package, and can be used to modify files very easily. Data base system development has progressed rapidly during the last two decades and the data managment system concepts used in this study reflect the advancement made in computer technology during this era. Urban stormwater data is, however, just one application for which the system can be used. (USGS)

Open-File Report↗