This thesis defines a methodology for the evaluation of the
worth of streamflow data using a Bayes risk approach. Using regional
streamflow data in a regression analysis, the Bayes risk can be computed
by considering the probability of the error in using the regionalized
estimates of bridge or culvert design parameters. Cost curves for over-
and underestimation of the design parameter can be generated based on
the error of the estimate. The Bayes risk can then be computed by integrating
the probability of estimation error over the cost curves. The
methodology may then be used to analyze the regional data collection effort
by considering the worth of data for a record site relative to the
other sites contributing to the regression equations.
The methodology is illustrated by using a set of actual streamflow
data from Missouri. The cost curves for over- and underestimation
of the streamflow design parameter for bridges and culverts are hypothesized
so that the Bayes risk might be computed and the results of the
analysis discussed. The results are discussed by demonstrating small
sample bias that is introduced into the estimate of the design parameter
for the construction of bridges and culverts. The conclusions are that
the small sample bias in the estimation of large floods can be substantial
and that the Bayes risk methodology can evaluate the relative worth
of data when the data are used in regionalization.
Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/617586 |
Date | 02 1900 |
Creators | Metler, William Arledge |
Contributors | Department of Hydrology & Water Resources, The University of Arizona |
Publisher | Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ) |
Source Sets | University of Arizona |
Language | en_US |
Detected Language | English |
Type | text, Technical Report |
Source | Provided by the Department of Hydrology and Water Resources. |
Rights | Copyright © Arizona Board of Regents |
Relation | Technical Reports on Hydrology and Water Resources, No. 16 |
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