A stochastic precipitation event model for use in erosion simulations Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/rj4307314

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  • Mathematical models of the precipitation process are needed to effectively use historical precipitation data in the design and planning of engineering projects and for analysis of watershed runoff behavior. Existing simple models based on the assumptions of independent, identically-distributed random variables are insufficient to describe several features of the historical precipitation process. The objective of this research was to develop a stochastic model of hourly precipitation that preserves the pattern of occurrence of precipitation events throughout the year as well as several characteristics of the duration, amount, and intensity of precipitation within events. Historical precipitation data for Salem, Oregon were analyzed to identify the patterns of occurrence of precipitation events and the dependence structure of the precipitation during events. The results of this analysis were used to guide the development of a general model of the precipitation process, based on the theory of stochastic cluster processes. The developed model consists of several parts. A Poisson cluster process is used to describe the pattern of occurrence of precipitation events. In this process, the time between clusters and the number of events within clusters are exponentially distributed random variables. The duration of events and the time between events within clusters are described with identical Logarithmic Negative Mixture distributions. The hourly precipitation amounts within events are described with a nonstationary, first-order autoregression model. To test the precipitation model, parameters were estimated for each month using 31 years of hourly precipitation data for Salem and Pendleton, Oregon, two stations from very different climatic regimes. The results of Monte Carlo simulations showed that the model accurately reproduces the seasonal pattern of event occurrence and the marginal and conditional distributions of the magnitude, duration, and intensity of precipitation during events. Autocorrelation functions for the historical and simulated data were also similar. It is concluded that the model has potential for use in many hydrologic applications that use historical precipitation data.
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