选用11种概率分布函数,分析了鄱阳湖流域“五河”6个水文站年最小连续7EI平均流量(或称极值流量)。概率分布函数的参数以及拟合优度分别由线性矩与柯尔莫哥洛夫一斯米尔诺夫方法(K—S法)检验,选出最适合该区极值流量分布函数,同时运用Mann Kendall(M-K)方法对极值流量趋势进行分析,对引起该流域水文极值变化的原因及其影响作了探讨。研究表明:(1)韦克比分布是用于研究鄱阳湖流域水文极值的最佳概率分布函数;(2)李家渡极值流量显著减少,1970年代以后枯水发生频率呈增加趋势;其余五站枯水流量呈增加趋势,枯水事件发生概率减小。其中,虎山和万家埠枯水在1980年代中期以后增加非常显著;外洲枯水流量多年变化最小,李家渡枯水流量多年变化最大;(3)降雨量是影响极值流量最重要的因素,水利工程和森林覆盖率变化有利于枯水流量的增加以及减少流量多年变化的不稳定性;而农业用地的增加显著减少枯水流量,易导致枯水流量变化的不稳定。
In this study, 11 probability distribution functions are adopted to systematically analyze the probability Behaviors of the 7-day low flow regimes (the minimum average flow for the consecutive 7 days) at six hydrological stations located in the "Five Rivers" in the Poyang Lake Basin. The L-moment technique is used to estimate the parameters of the probability functions and the Kolmogorov-Smirnov method (K-S) is accepted to evaluate the goodness-of-fit of the probability functions. Trends within the 7-day low flow series are tested using the Mann-Kendall (M-K) method. The results show that: (1) Wake dis- tribution is the candidate distribution function with the highest goodness-of-fit in the study of the extreme flow regimes over the Poyang Lake Basin; (2) The g-day low flow is in sig- nificant decrease at Lijiadu station. The frequency of the 7-day low flow regimes tends to increase after the 1970s; The g-day low flow at the other five stations is in an increasing trend and the probability of dry episodes is decreasing. The 7-day low flow at Hushan and Wanjiabu stations is in significant increase in the mid-1980s; Moreover, variability of the g-day low flow at Waizhou station is the smallest, and that at Lijiadu station is the lar- gest; (3) Precipitation changes are one of the major factors influencing the 7-day low flow changes. Hydraulic facilities are helpful to the decrease of the streamflow magnitude. Be- sides, forestation can flatten the variability of the 7-day low flow changes. The results of this study could be of scientific and practical merits in terms of good understanding of ex treme flow changes under the influences of climate changes and human activities.