JOURNAL OF NATURAL RESOURCES ›› 2010, Vol. 25 ›› Issue (6): 1033-1041.doi: 10.11849/zrzyxb.2010.06.015

• Resources Research Methods • Previous Articles     Next Articles

Statistic Markovian Model for Predicting of Annual Precipitation

MA Zhan-qing1,XU Ming-xian1,YU Wei-yang1,WEN Shu-yao2   

  1. 1. Hangzhou Vocation and Technical College, Hangzhou 310018, China;
    2. School of Geography, Beijing Normal University, Beijing 100875, China
  • Received:2009-10-27 Revised:2010-03-25 Online:2010-06-30 Published:2010-06-30

Abstract:

Based on the speciality of uncertainty and inaccuracy of precipitation, both simple statistical calculation and Markov chain theory were used together for predicting the precipitation in this paper. The method was characteristic of clear physical concept and simple calculation. Data of precipitation in Hangzhou, from 1956 to 2008, was used as an example. The precipitation can be predicted year by year using the statistical models;starting from the precipitation sequence data (1956—1995) in the last 40 years, the precipitation in 1996 was predicted, then the precipitation data in 1956 was removed and the actual data in 1996 was added to the sequence, and then in accordance with the basic steps of precipitation forecasts, precipitation in 1997 was predicted, and so on for each year of precipitation forecasts. Results show that the error value smaller than ±10%, ±15% and ±20% was 30.77%, 53.85% and 69.23% respectively in the 13 years of precipitation prediction, and the maximum error value of the prediction was -24.03%. The precipitation can be predicted year by year using the statistical Markonian models;the error value smaller than ±1% and ±5% was 37.50% and 62.50% respectively in the 8 years of precipitation prediction, and the maximum error value of the prediction was 8.77%. The accuracy of precipitation prediction of the statistical Markonian models was improved obviously, hence a practicable method for predicting future precipitation was put forward.

Key words: hydrology and water resources, precipitation, Markonian prediction model

CLC Number: 

  • P333