Forecasting Characteristic Water Levels of Taihu Lake Based on Time Series Decomposing Method

YIN Yi-xing; XU You-peng; ZHANG Xiao-na; JIAO Shi-xing; YAN Gui-xia

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Forecasting Characteristic Water Levels of Taihu Lake Based on Time Series Decomposing Method

- Forecasting Characteristic Water Levels of Taihu Lake Based on Time Series Decomposing Method
- Bulletin of Soiland Water Conservation Vol. 31, Issue 4, Pages: 172-175(2012)
- 作者机构：
  
  1. 南京信息工程大学应用水文气象研究院,江苏,南京,210044
  2. 南京大学地理与海洋科学学院,江苏,南京,210093
  3. 河海大学水文水资源与水利工程科学国家重点实验室,江苏,南京,210098
  4. 安阳师范学院资源环境与旅游学院,河南,安阳,455002
- 作者简介：
- 基金信息：
- DOI：
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- Published：2012
- 稿件说明：
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YIN Yi-xing, XU You-peng, ZHANG Xiao-na, et al. Forecasting Characteristic Water Levels of Taihu Lake Based on Time Series Decomposing Method[J]. Bulletin of Soiland Water Conservation, 2012, 31(4): 172-175.
DOI：

YIN Yi-xing, XU You-peng, ZHANG Xiao-na, et al. Forecasting Characteristic Water Levels of Taihu Lake Based on Time Series Decomposing Method[J]. Bulletin of Soiland Water Conservation, 2012, 31(4): 172-175. DOI：

摘要

基于太湖沿湖5个水文台站1956—2006年的逐日平均水位数据，提取得到了逐年特征(平均、最高和最低)水位，运用时间序列分解方法模拟和预测了太湖未来15a的特征水位，并探讨了太湖未来洪灾情势．将水位序列分解为趋势成分、周期成分和随机成分，得到时间序列分解模型，并进行了水位序列模拟和预测．分析结果表明，该模型的模拟精度比较理想，可以实现未来较长期的特征水位预测．预测得到未来15a后的最高水位可能达到4.05m，显著超过太湖警戒水位，因此需引起相关部门的重视．

Abstract

Based on the daily average water levels of the five gauges around the Taihu Lake observed from 1956 to 2006

the annual characteristic water levels(average

highest and lowest) were obtained. Time series analysis method was adopted

and the decomposition model of trend

period and random components was applied to simulate and fore-cast the annual characteristic water levels of Taihu Lake. The annual characteristic water levels were forecasted for the next 15 years and the future flood scenarios of Taihu Lake basin were also addressed. The results in-dicate that the highest water level of Taihu Lake may reach 4.05 m in 2021

which is significantly higher than the warning water level of Taihu Lake and thus worthy of attention for relevant department.

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