MULTISITE GENERALIZATION OF A DAILY STOCHASTIC PRECIPITATION GENERATION MODEL

The familiar chain-dependent-process stochastic model of daily precipi tation, consisting of a two-state, first-order Markov chain for occurr ences and a mixed exponential distribution for nonzero amounts, is ext ended to simultaneous simulation at multiple locations by driving a co llection of individual models with serially independent but spatially correlated random numbers. The procedure is illustrated for a network of 25 locations in New York state, with interstation separations rangi ng approximately from 10 to 500 km. The resulting process reasonably r eproduces various aspects of the joint distribution of daily precipita tion observations at the modeled locations. The mixed exponential dist ributions, in addition to providing substantially better fits than the more conventional gamma distributions, are convenient for representin g the tendency for smaller amounts at locations near the edges of wet areas. Means, variances, and interstation correlations of monthly prec ipitation totals an also well reproduced. In addition, the use of mixe d exponential rather than gamma distributions yields interannual varia bility in the synthetic series that is much closer to the observed. (C ) 1998 Elsevier Science B.V. All rights reserved.