应用极点对称模态分解分析径流时空演化规律

研究径流演化规律对水文预报和水资源合理配置具有重要意义。鉴于径流序列具有多时间尺度与非线性趋势混叠的特征，利用自适应、无基的极点对称模态分解（ESMD）将长江上游干支流8站的年、月、日径流序列分解为各模态分量和趋势余项，甄别大尺度循环和非线性趋势；利用周期图、趋势余项、时频分析，同时掌握径流周期、趋势、突变规律，并与现有方法对比分析。结果表明：时间上，主要存在2年、6年、10 ~ 11年的年际与6个月、1年、2年、4年、22 ~ 23年的年内周期规律。空间上，除朱沱站年内、年际径流呈增加趋势，其余站径流均呈减少趋势；8站的突变规律也不完全相同。ESMD能同时从周期、趋势、突变来挖掘径流水文特性，为径流时空演化规律分析开辟新思路。

Spatiotemporal evolution of runoff based on extreme-point symmetric mode decomposition

Studying the evolution trends of river runoff is of great significance for hydrological forecasting and rational allocation of water resources. In view of the multi-time scales and non-linear trend aliasing in runoff sequences, this paper presents an application of extreme-point symmetric mode decomposition (ESMD) to analysis of the spatiotemporal evolution of runoffs for the upper reach of the Yangtze River. Using ESMD, we decompose runoff sequences into various modes, including the annual, monthly, and daily runoff sequences for eight hydrological stations on the mainstream and tributaries of the reach. Then, we identify large-scale cyclic and non-linear trends using state components and trend residuals; obtain the sequences’ time periods, variation trends, and abrupt changes though examining the cycle diagrams, trend residuals, and time frequency analysis; and compare with previous methods. The results show that temporally, two types of dominant cycles are observable: annual cycles of 6 months, 1 year, 2 years, 4 years, and 22-23 years; and inter-annual cycles of 2 years, 6 years, and 10-11 years. Spatially, all the stations show a decreasing trend in the annual and inter-annual runoffs, except for the Zhutuo station featured with an increasing trend. The abrupt change patterns at the eight stations are not the same. This study demonstrates ESMD can excavate more hydrological characteristics of runoff through identifying the cycles, trends and mutations, opening up new ideas for spatiotemporal evolution analysis of river runoff.