基于局部均值分解和最大熵谱估计的径流分析

Runoff analysis based on local mean decomposition and maximum entropy spectrum estimation

研究径流序列的变化规律是水文预报的重点,如何将非平稳非线性的径流序列分解为平稳序列是当前水文领域的研究难点之一。基于此建立局部均值分解和最大熵谱估计耦合模型,以密云水库上游控制入库的下会站月径流序列为例,尝试将局部均值分解用于径流序列的时频分解,并采用最大熵谱估计分析各分量频率,以检验分解结果,获取径流序列更多有效频率信息。结果表明:局部均值分解能够对下会站径流序列进行分解且分解结果良好,各分量在最大熵谱检验中均呈现出一个主要周期,其对应值分别为2个月、6个月、18个月、5年、11年和22年。该方法在实际案例中的成功应用表明局部均值分解和最大熵谱估计耦合模型能够为水文时间序列分析及预测提供新的思路。

Analyzing the variation trends in runoff series is a basis for hydrological forecasting.How to decompose a nonstationary nonlinear runoff series into a stationary series becomes a difficult issue in the current research of hydrology.This paper presents a coupling model of local mean decomposition(LMD)for time-frequency decomposition and maximum entropy spectral estimation(MESE)for the frequencies of each decomposed component.Monthly runoff series of the Xiahui hydrometric station,located upstream of the Miyun Reservoir,is used to verify the decomposition results and extract more effective frequency information from these series.The results show that LMD gives good decomposition results for the series,and MESE reveals one dominant cycle in each of the components:2 months,6 months,18 months,5 years,11 years,and 22 years respectively.This demonstrates LMD-MESE coupling is a new useful approach to analysis and prediction of hydrological time series.