Geology topics

James R. Slack

Publications and source records attributed to James R. Slack.

7 recordsLinked to original sources

USGS2011

USGS library for S-PLUS for Windows -- Release 4.0

Release 4.0 of the U.S. Geological Survey S-PLUS library supercedes release 2.1. It comprises functions, dialogs, and datasets used in the U.S. Geological Survey for the analysis of water-resources data. This version does not contain ESTREND, which was in version 2.1. See Release 2.1 for information and access to that version. This library requires Release 8.1 or later of S-PLUS for Windows. S-PLUS is a commercial statistical and graphical analysis software package produced by TIBCO corporation(http://www.tibco.com/). The USGS library is not supported by TIBCO or its technical support staff.

Open-File Report↗

USGS2010

Techniques of trend analysis for monthly water quality data

Some of the characteristics that complicate the analysis of water quality time series are non-normal distributions, seasonality, flow relatedness, missing values, values below the limit of detection, and serial correlation. Presented here are techniques that are suitable in the face of the complications listed above for the exploratory analysis of monthly water quality data for monotonie trends. The first procedure described is a nonparametric test for trend applicable to data sets with seasonality, missing values, or values reported as ‘less than’: the seasonal Kendall test. Under realistic stochastic processes (exhibiting seasonality, skewness, and serial correlation), it is robust in comparison to parametric alternatives, although neither the seasonal Kendall test nor the alternatives can be considered an exact test in the presence of serial correlation. The second procedure, the seasonal Kendall slope estimator, is an estimator of trend magnitude. It is an unbiased estimator of the slope of a linear trend and has considerably higher precision than a regression estimator where data are highly skewed but somewhat lower precision where the data are normal. The third procedure provides a means for testing for change over time in the relationship between constituent concentration and flow, thus avoiding the problem of identifying trends in water quality that are artifacts of the particular sequence of discharges observed (e.g., drought effects). In this method a flow-adjusted concentration is defined as the residual (actual minus conditional expectation) based on a regression of concentration on some function of discharge. These flow-adjusted concentrations, which may also be seasonal and non-normal, can then be tested for trend by using the seasonal Kendall test.

Water Resources Research↗

USGS2010

A nonparametric trend test for seasonal data with serial dependence.

Statistical tests for monotonic trend in seasonal (e.g., monthly) hydrologic time series are commonly confounded by some of the following problems: nonnormal data, missing values, seasonality, censoring (detection limits), and serial dependence. An extension of the Mann-Kendall test for trend (designed for such data) is presented here. Because the test is based entirely on ranks, it is robust against nonnormality and censoring. Seasonality and missing values present no theoretical or computational obstacles to its application. Monte Carlo experiments show that, in terms of type I error, it is robust against serial correlation except when the data have strong long-term persistence (e.g., ARMA (1, 1) monthly processes with ϕ > 0.6) or short records (∼ 5 years). When there is no serial correlation, it is less powerful than a related simpler test which is not robust against serial correlation.

Water Resources Research↗

USGS2006

Computer program for the Kendall family of trend tests

The Seasonal Kendall (SK) test for trend was developed by the U.S. Geological Survey and has become the most frequently used test for trend in the environmental sciences. Recently the test was modified to form the Regional Kendall (RK) test for trend. In this form, trends at numerous locations within a region are tested to determine whether the direction of trend is consistent across the entire region. Computer code developed at the USGS in the 1980s to perform the SK test is no longer widely available. Other versions written by other scientists may or may not be easily available, and may require commercial software in order to be run. These other versions do not explicitly compute the RK test. Therefore, the original code for computing the SK test has been repackaged into a program that runs under the Windows operating system. This program may be used to verify that other implementations of the test give the same results as the original. The program also provides a means for computing the RK test and the simpler Mann-Kendall test for trend.

Scientific Investigations Report↗

USGS1998

Data from selected U.S. Geological Survey National Stream Water Quality Monitoring Networks

A nationally consistent and well-documented collection of water quality and quantity data compiled during the past 30 years for streams and rivers in the United States is now available on CD-ROM and accessible over the World Wide Web. The data include measurements from two U.S. Geological Survey (USGS) national networks for 122 physical, chemical, and biological properties of water collected at 680 monitoring stations from 1962 to 1995, quality assurance information that describes the sample collection agencies, laboratories, analytical methods, and estimates of laboratory measurement error (bias and variance), and information on selected cultural and natural characteristics of the station watersheds. The data are easily accessed via user-supplied software including Web browser, spreadsheet, and word processor, or may be queried and printed according to user-specified criteria using the supplied retrieval software on CD-ROM. The water quality data serve a variety of scientific uses including research and educational applications related to trend detection, flux estimation, investigations of the effects of the natural environment and cultural sources on water quality, and the development of statistical methods for designing efficient monitoring networks and interpreting water resources data.

Water Resources Research↗

USGS1983

Nonparametric tests for trends in water-quality data using the statistical analysis system

Two nonparametric procedures to test for trends in water-quality data (SEASKEN AND SEASRS) have been developed for the Statistical Analysis System* (SAS). The procedure SEASKEN tests for a monotonic trend in time by a modified form of Kendall's tau, the Seasonal Kendall test. The procedure SEASRS tests for a step trend between two different periods in a time series using a modified form of the Wilcoxon (Mann-Whitney) rank sum test, the Mann-WhitneyWilcoxon rank sum test for seasonal data. Examples are presented using the two procedures. The source code and user's guide for each of the two procedures are also presented. Procedures for flow adjusting water-quality data by the SAS procedures REG and SYSREG and techniques for plotting water-quality data as a time series by the SAS procedure PLOT are presented. Additionally, examples are presented to demonstrate the use of the U.S. Geological Survey procedures QWRETR, DVRETR, QWSAS, and DVINPUT to retrieve data from the Geological Survey WATSTORE system and make it available to SAS.

Open-File Report↗

USGS1978

An oilspill risk analysis for the eastern Gulf of Mexico (proposed sale 65) Outer Continental Shelf lease area

An oilspill risk analysis was conducted to determine the relative environmental hazards of developing oil in different regions of the Eastern Gulf of Mexico Outer Continental Shelf lease area. The study analyzed the probability of spill occurrence, likely paths of the spills, and locations in space and time of such objects as recreational and biological resources likely to be vulnerable. These results combined to yield estimates of the overall oilspill risk associated with development of the proposed lease area. This risk is compared to the existing oilspill risk from existing leases in the area. The analysis implicitly includes estimates of weathering rates and slick dispersion and an indication of the possible mitigating effects of cleanups.

Gulf Of Mexcio↗