基于水位低通滤波的入库流量反推方法研究

Study on reverse deduction method of reservoir inflow based on water level low-pass filtering

摘要

【目的】准确的入库流量数据是水库调度运行及水文预报依赖的重要基础数据，但经由水量平衡法直接推求的入库流量过程通常呈锯齿震荡且存在大量异常负值，现有通用方法在平滑效果及适用性方面还有待进一步提升。【方法】从造成入库流量锯齿震荡的直接原因入手，采用低通滤波方法，除去掺杂在水位数据中影响水位库容计算的高频噪声，减少其对入库流量推求的影响。【结果】结果显示：(1)对于整个流量过程，水位低通滤波方法计算所得入库流量数据中的负值占比仅为3.20%,负值均值仅为-29.76 m³/s,平滑改善度为0.77,表现最佳，滑动平均法次之，五点三次法最差；(2)对于不同量级典型洪水过程，低通滤波方法在保证了较好平滑改善度的前提下减少了平滑处理对洪峰和峰现时间的影响。【结论】结果表明：基于水位低通滤波的入库流量反推方法计算原理简单，在保证了较好平滑改善度的前提下，大大减少了入库流量过程中的异常负值，同时对洪峰和峰现时间的影响较小，可以有效提升入库流量反推结果的合理性。

Abstract

[Objective] Accurate reservoir inflow data is essential for effective reservoir operation scheduling and hydrological forecasting. However, the inflow process directly derived using the water balance method typically exhibits sawtooth oscillations and contains numerous anomalous negative values. Existing general method still require further improvement in both smoothing effectiveness and applicability. [Methods] Starting with the direct cause of sawtooth oscillations in the reservoir inflow, a low-pass filtering method was used to remove the high-frequency noise in the water level data that affected the calculation of reservoir storage capacity, thereby minimizing its impact on the estimation of inflow. [Results] The result demonstrated that:(1) for the entire flow process, the low-pass filtering method for water level data showed the best performance, with only 3.20% of the calculated inflow data having negative values, an average negative value of-29.76 m³/s, and a smoothing improvement index of 0.77. The moving average method ranked second, and the five-point cubic method performed the worst.(2) For typical flood processes of different magnitudes, the low-pass filtering method reduced the impact of smoothing on the peak flow and the time of peak occurrence, while still achieving a good smoothing improvement. [Conclusion] The result indicate that the reverse deduction method of reservoir inflow based on water level low-pass filtering is characterized by its simplicity in computation and principle. While maintaining a good smoothing improvement, it significantly reduces the anomalous negative values in the inflow process. Additionally, it has a relatively small impact on the peak flow and the time of peak occurrence, which can effectively improve the rationality of the inflow deduction result.

关键词

入库流量 / 数据平滑 / 低通滤波 / 水库水位 / 影响因素

Key words

reservoir inflow / data smoothing / low-pass filtering / reservoir water level / influencing factors

基金资助

中国长江三峡集团有限公司科研项目(WWKY-2021-0485)

国家自然科学基金项目(42201050)