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A Markov Chain Model of daily rainfall
Author(s) -
Haan C. T.,
Allen D. M.,
Street J. O.
Publication year - 1976
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr012i003p00443
Subject(s) - markov chain , stochastic matrix , markov model , statistics , environmental science , term (time) , markov process , econometrics , mathematics , computer science , meteorology , geography , physics , quantum mechanics
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.