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The design of many water resources projects requires knowledge of possible long‐term rainfall patterns. A stochastic model based on a first‐order Markov chain was developed to simulate daily rainfall at a point. The model uses historical rainfall data to estimate the Markov transitional probabilities. A separate matrix is estimated for each month of the year. In this research, 7 × 7 transitional probability matrices were used. The model is capable of simulating a daily rainfall record of any length, based on the estimated transitional probabilities and frequency distributions of rainfall amounts. The simulated data have statistical properties similar to those of historical data.
Haan et al. (Tue,) studied this question.
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