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首页> 外文期刊>Journal of Hydroinformatics >Multivariate and multi-scale generator based on non-parametric stochastic algorithms
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Multivariate and multi-scale generator based on non-parametric stochastic algorithms

机译：基于非参数随机算法的多变量和多尺度发电机

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摘要

A method for generating combined multivariate time series at multiple locations and at different time scales is presented. The procedure is based on three steps: first, the Monte Carlo method generation of data with statistical properties as close as possible to the observed series; second, the rearrangement of the order of simulated data in the series to achieve target correlations; and third, the permutation of series for correlation adjustment between consecutive years. The method is non-parametric and retains, to a satisfactory degree, the properties of the observed time series at the selected simulation time scale and at coarser time scales. The new approach is tested on two case studies, where it is applied to the log-transformed streamflow and precipitation at weekly and monthly time scales. Special attention is given to the extrapolation of non-parametric cumulative frequency distributions in their tail zones. The results show a good agreement of stochastic properties between the simulated and observed data. For example, for one of the case studies, the average relative errors of the observed and simulated weekly precipitation and streamflow statistics (up to skewness coefficient) are in the range of 0.1-9.2% and 0-5.4%, respectively.
机译：提出了一种在多个位置和不同时间尺度处生成组合多变量时间序列的方法。该程序基于三个步骤：首先，Monte Carlo方法生成具有统计属性的数据，尽可能接近观察到的系列;其次，串联中的模拟数据顺序重新排列以实现目标相关;第三，连续几年之间的相关调整序列置换。该方法是非参数的并且保留，以令人满意的程度，在所选模拟时间尺度和较粗糙的时间尺度下观察时间序列的特性。在两个案例研究中测试了新方法，其中它适用于每周和每月时间尺度的对数转换的流流和降水。特别注意其尾部区域的非参数累积频率分布的外推。结果表明模拟和观察数据之间的随机性能吻合良好。例如，对于其中一个案例研究，观察和模拟的每周降水和流流统计（最高偏斜系数）的平均相对误差分别为0.1-9.2％和0-5.4％。

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