Analysis and Prediction of Annual Precipitation Based on Weighted Markov Chain in Typical Region of Taihu Lake Basin

ZHANG Congcong; CHEN Xiaomin; CHEN Xu; YU Kaihao; PAN Genxing; ZHANG Xuhui

doi:10.13961/j.cnki.stbctb.2015.01.031

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Analysis and Prediction of Annual Precipitation Based on Weighted Markov Chain in Typical Region of Taihu Lake Basin

- Analysis and Prediction of Annual Precipitation Based on Weighted Markov Chain in Typical Region of Taihu Lake Basin
- Bulletin of Soiland Water Conservation Vol. 35, Issue 1, Pages: 163-168(2015)
- 作者机构：
  
  南京农业大学资源与环境科学学院,江苏,南京,210095
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2015.01.031
  CLC：
- Published：2015
- 稿件说明：
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ZHANG Congcong, CHEN Xiaomin, CHEN Xu, et al. Analysis and Prediction of Annual Precipitation Based on Weighted Markov Chain in Typical Region of Taihu Lake Basin[J]. Bulletin of Soiland Water Conservation, 2015, 35(1): 163-168.
DOI：

ZHANG Congcong, CHEN Xiaomin, CHEN Xu, et al. Analysis and Prediction of Annual Precipitation Based on Weighted Markov Chain in Typical Region of Taihu Lake Basin[J]. Bulletin of Soiland Water Conservation, 2015, 35(1): 163-168. DOI： 10.13961/j.cnki.stbctb.2015.01.031.

摘要

[目的] 为了探讨如何预测太湖流域的年降水量

发现年降水量的规律。[方法] 对太湖流域1960-2010年年降水量进行了分析

运用加权马尔可夫链方法建立了该区年降水量预测模型。[结果] 太湖流域20世纪60

70和80年代研究区的年平均降水量均低于多年平均降水量;该降水序列满足马尔可夫链的要求。建立了加权马尔可夫链模型

并预测了2011年和2012年的年降水量

预测值与实测值的相对误差分别为6.59%和-10.74%;平水年、偏枯年、偏丰年、枯水年和丰水年出现的概率分别为0.2685

0.2067

0.1845

0.1661和0.1742。[结论] 马尔科夫链预测平水年的年降水量相对误差较小

偏枯年和丰水年年降水量的相对误差较大;平水年、偏枯年和偏丰年出现的概率较大。

Abstract

[Objective] To discuss how to forecast annual precipitation of the Taihu Lake basin and find regularity of annual precipitation.[Methods] Annual precipitation data of Taihu Lake basin from 1960 to 2010 were analyzed. The annual rainfall forecast model was established by the weighted Markov chain.[Results] The precipitation of the Taihu Lake basin was below average precipitation for years in the 1960s

1970s and 1980s. Based on the verification of the Markov chain characteristics of precipitation

the weighted Markov chain model was used to predict the annual precipitation state and annual rainfall of 2011 and 2012

the relative errors were 6.59% and -10.74%. Analysis on Markov chain of various step size showed that the appearance probability of normal flow year

partial low flow year

partial flow year

low flow year and wet year was 0.268 5

0.206 7

0.184 5

0.166 1 and 0.174 2

respectively.[Conclusion] The relative errors of weighted Markov chain precipitation simulation from 2002 to 2011 shows that the relative errors of wet year and partial low flow year are bigger and the normal flow year is smaller. The appearance probability of normal flow year

partial low flow year and partial flow year are larger.

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