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William H. Kirby

Publications and source records attributed to William H. Kirby.

5 recordsLinked to original sources
USGS2010

The linear decision rule in reservoir management and design: 1, Development of the stochastic model

With the aid of a linear decision rule, reservoir management and design problems often can be formulated as easily solved linear programing problems. The linear decision rule specifies the release during any period of reservoir operation as the difference between the storage at the beginning of the period and a decision parameter for the period. The decision parameters for the entire study horizon are determined by solving the linear programing problem. Problems may be formulated in either the deterministic or the stochastic environment.

Water Resources Research↗
USGS2009

The scientific assessment and strategy team contributions assessing the 1993 flood on the Mississippi and Missouri River basins

The Scientific Assessment and Strategy Team was formed to provide scientific advice and assistance to federal officials responsible for making decisions with respect to flood recovery in the Upper Mississippi River Basin (above Cairo, Illinois) as a result of the 1993 flooding. The team assembled data from a wide variety of sources within federal, state, and local governments, and the private sector. The huge volume of data (over 250 gigabytes) made analysis of all the data difficult. The locating, obtaining, and conversion of the data were extremely difficult leading to a recommendation for an on-line source or at least an on-line listing of available data. Parts of the data were analyzed and scientific bases were establishedfor discussions concerning levee effects on flood stages, effects of wetlands on flooding , effects of man 's increased intervention in the floodplains and uplands, and proposals regarding floodways on the nations rivers. The data collected indicated that the 1993 flooding could not be attributed to man's intervention on the floodplain but to the excessive amount of rainfall in the basin. The stormpatterns thatproduced the rainfall also moved south at about the same speed as the flood waters resulting in storms later in the summer reinforcing early floods. This resulted in a very long duration flood that overwhelmed existing flood reduction measures in the portions of the basin that were flooded.

Mississppi River basin, Missouri River basin↗
USGS2006

User's manual for Program PeakFQ, annual flood-frequency analysis using Bulletin 17B guidelines

Estimates of flood flows having given recurrence intervals or probabilities of exceedance are needed for design of hydraulic structures and floodplain management. Program PeakFQ provides estimates of instantaneous annual-maximum peak flows having recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years (annual-exceedance probabilities of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002, respectively). As implemented in program PeakFQ, the Pearson Type III frequency distribution is fit to the logarithms of instantaneous annual peak flows following Bulletin 17B guidelines of the Interagency Advisory Committee on Water Data. The parameters of the Pearson Type III frequency curve are estimated by the logarithmic sample moments (mean, standard deviation, and coefficient of skewness), with adjustments for low outliers, high outliers, historic peaks, and generalized skew. This documentation provides an overview of the computational procedures in program PeakFQ, provides a description of the program menus, and provides an example of the output from the program.

Techniques and Methods↗
USGS1974

Straight line fitting of an observation path by least normal squares

In curtain hydro graphic problems, and perhaps in other geophysical problems, information must be collected as profiles along straight-line courses. When the inevitable deviations from perfect linearity occur, one must then find the straight line course that best approximates the actual observation path. Classical least squares (regression) does not solve this problem, because the line thus fitted depends upon which coordinate is taken as the dependent variable. A coordinate-free solution is obtained by minimizing the sum of squares of normal distances between the line and the observation points. As in classical least squares, the line of best fit passes through the geometrical centroid of the observation points. The slope of this line, however, is closer to 1.0 than the slope of the classical regression line. The least normal squares and classical least squares solutions coincide when the observation path is nearly perfectly straight or when it runs generally parallel to one of the coordinate axes.

Open-File Report↗