基于马尔可夫链的太湖流域典型地区年降水量分析与预测  

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

作　　者：张聪聪[1] 陈效民[1] 陈旭[1] 虞凯浩 潘根兴[1] 张旭辉[1] 

机构地区：[1]南京农业大学资源与环境科学学院,江苏南京210095

出　　处：《水土保持通报》2015年第1期163-168,175,共7页Bulletin of Soil and Water Conservation

基　　金：国家公益性行业(农业)科研专项"气候变化对农业生产的影响及应对技术研究"(200903003);江苏高校优势学科建设工程项目

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0.268 5,0.206 7,0.184 5,0.166 1和0.174 2。[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。[Objective] To discuss how to forecast annual precipitation of the Taihu Lake basin and find regu- larity 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.

关 键 词：太湖流域 加权马尔可夫链 年降水量 中长期预报 

分 类 号：P463.23[天文地球—大气科学及气象学] S152.7[农业科学—土壤学]