Increasing concern about the accuracy of hydrologic and water quality models has prompted interest in procedures for evaluating the uncertainty associated with these models. If a Monte Carlo simulation is used in an uncertainty analysis, assumptions must be made relative to the probability distributions to assign to the model input parameters. Some have indicated that since these parameters can not be readily determined, uncertainty analysis is of limited value. In this article we have evaluated the impact of parameter distribution assumptions on estimates of model output uncertainty. We conclude that good estimates of the means and variances of the input parameters are of greater importance than the actual form of the distribution. This conclusion is based on an analysis using the AGNPS model.

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This article is concerned with using the E-Bayesian method for computing estimates of the unknown parameter of Lomax distribution. These estimates are derived based on a conjugate prior for the parameter under squared error loss function and Linex loss function. A comparison between this method and the corresponding Bayes and maximum likelihood techniques is conducted using Monte Carlo simulation.

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