挠力河流域径流变化特征与影响因素分析

挠力河作为三江平原最大的流域,其径流变化过程对三江平原的社会经济发展、生态环境变化起着至关重要的作用。为获取该流域的径流量变化特征,首先采用线性趋势分析法和Mann-Kendall趋势检验法对流域4个代表性水文站径流序列进行趋势分析,然后采用滑动移除近似熵法(MC-Ap En法)和Mann-Kendall突变检验法分别对径流进行突变检验,并对径流发生变化的原因进行了探讨。结果表明:挠力河流域上、中、下游径流量分布不均匀,且同一水文站径流年际变化较大,不利于挠力河流域水资源的开发利用;挠力河流域近50 a来径流量呈明显的减少趋势,其中宝清站、保安站、菜咀子站径流下降趋势明显;挠力河径流量在1966年发生突变,突变后径流呈明显的减少趋势,发生突变的主要原因是降雨量减少、农业开垦的扩张以及水利工程建设的日益增多;滑动移除近似熵法在进行径流突变分析的过程中,表现出精确度高、抗干扰能力强等特点,是一种适于径流序列突变检测的方法。

Analysis of Runoff Variation and Impacting Factors in Naoli River Basin

The Naoli River Basin is the largest basin on Sanjiang Plain. Its runoff variation plays a crucial role in the development of social economy and ecological environment change of Sanjiang Plain. In order to obtain the variation characteristics of runoff series, the linear trend analysis and Mann-Kendall test were used to analyze the trend of runoff series of four representative hydrological stations in Naoli River Basin. Then the moving cut data-approximate entropy (MC-ApEn method) and Mann-Kendall mutation testing were used to check the mutation characteristics of runoff series. Also the mutation reason of the runoff series was discussed. The results showed that the runoff volume of Naoli River was distributed unevenly and the annual runoff of the same hydrological station had great variation. It was not conducive to the full use of water resources in Naoli River Basin. Runoff series showed obvious reducing trend in the recent 50 years, and the runoff of the Baoqing Station, the Baoan Station and the Caizuizi Station decreased obviously. The runoff mutations of Naoli River occurred in 1966, after the abrupt change, there is an obviously decreasing trend of runoff which is large because of reduced precipitation, increased urban residents water consumption and the growing number of construction of water conservancy. The moving cut data-approximate entropy in the process of runoff mutation analysis showed the characteristics of high precision, strong anti interference ability and so on and it is a method for detecting the mutations of nonlinear time series.