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The Uniformization of Annual Maximum 1・2・3 Day Precipitations and its Nonstationary Frequency Analysis

年最大1·2·3日降水量的均匀化及其非平稳频率分析

基本信息

批准号：
08650595
负责人：
SOGAWA Noriaki
金额：
$ 1.41万
依托单位：
Shinshu University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1996
资助国家：
日本
起止时间：
1996 至 1997
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-08650595/
关键词：
Nonhomogeneity Nonstationarity Parameter Time Series Hydrological Variable Annual Maximum 1・2・3 Day Precipitations Front Typhoon Cyclone ヒストグラム時系列

项目摘要

This study put in operation the nonstationary frequency analysis by using the uniformized annual maximum 1・2・3 day precipitations. The summary of research results is as follows.1. The approximation of parameter time series(1) The annual maximum 1・2・3 day precipitations of front, typhoon, cyclone and non-separation for 29 years from 1964 to 1992 were used in Nagano, Matsumoto, Iida, Suwa and Karuizawa stations as real data. These precipitation time series had the nonstationarity.(2) When time expresses t, Gumbel distribution, which is assumed as the population distribution, has the nonstationary scale parameter a (t) and location parameter b (t). The time series of these parameters obtained by 5 year moving partial data (it is called parameter time series) were approximated by the periodic component and the trend component.(3) The nonstationary parameter time series of Gumbel distribution were approximated by the logistic curve similarly.2. The estimation of nonstationary hydrological variable(1) The hydrological variables of exceedance by using the parameter time series in 1.(2) were estimated, and the time series of hydrological variable were drawn. It had the nonstationality.(2) The hydrological variables of exceedance by using the parameter time series in 1.(3) were estimated, and the time series of hydrological variable were drawn similarly. It had the nonstationality too.3. The examination of the stability of parameter time seriesThe length of moving partial data was 5 years in this study. This stability was examined by simulation. The result said that 5 year moving partial data did not give the sufficient estimated value of parameter time series.

利用均匀年最大1·2·3日降水量进行了非平稳频率分析。研究成果总结如下：1.研究成果综述。参数时间序列的近似：(1)利用长野、松本、井田、须和和Karuizawa站1964~1992年29年锋面、台风、气旋和不分离的年最大1·2·3d降水量作为实际资料。(2)当时间表示t时，假设为总体分布的冈贝尔分布具有非平稳尺度参数a(T)和位置参数b(T)。用5年移动部分数据得到的这些参数的时间序列(称为参数时间序列)分别用周期分量和趋势分量来逼近。(3)用Logistic曲线近似Gumbel分布的非平稳参数时间序列。非平稳水文变量的估计(1)利用文[1]中的参数时间序列对超越的水文变量进行估计。(2)绘制水文变量的时间序列。(2)利用文献[1]中的参数时间序列估计了超越的水文变量。(3)对水文变量的时间序列进行了类似的绘制。它也具有非平稳性。参数时间序列稳定性检验本研究的移动部分数据长度为5年。通过仿真验证了该方法的稳定性。结果表明，5年移动部分数据不能给出参数时间序列足够的估计值。