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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Published in Transactions of the ASAE, v. 41, issue 1, p. 65-70.

© 1998 American Society of Agricultural Engineers

The copyright holder has granted the permission for posting the article here.

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The work reported here was supported in part through the Oklahoma State University Water Resources Research Institute by the U.S. Department of Interior as authorized by the Water Resources Development Act of 1979 (p.L. 95-467); The U.S. Department of Agriculture, CSRS, Grants Program; and the Oklahoma Agricultural Experiment Station as a contribution to Regional Project S-249.