The stochastic approach to watershed modeling refers to the techniques used to generate synthetic hydrologic data. These data may be used either for input to a parametric watershed model or to provide directly an estimate of the output of a hydrologic process. In both cases the basic techniques of the generation processes are the same. The type of process depends primarily on the purpose for which the data are being generated and on the quality and quantity of sample data.
Techniques are presented which can be used to generate data for one or any number of variates. The data generated can be normal, skewed, or log normal, and include serial correlation. If two or more variates are involved, cross correlation may also be considered.
Brief discussions concerning missing data, statistical tests, random number generation, and interpretation of results are presented along with a review of the generation schemes that have been used in the stochastic generation of hydrologic data.