变化环境下海河流域天然河川径流丰枯概率演变规律  

Evolution law of wet and dry probability of natural river runoff in Haihe River basin under changing environment

作　　者：鲁帆[1] 江明 蒋云钟[1] 周毓彦[1] 徐扬 LU Fan;JIANG Ming;JIANG Yunzhong;ZHOU Yuyan;XU Yang(Department of Water Resources,China Institute of Water Resources and Hydropower Research,Beijing 100038,China;School of Resources and Earth Science,China University of Mining and Technology,Xuzhou 221116,China)

机构地区：[1]中国水利水电科学研究院水资源研究所,北京100038 [2]中国矿业大学资源与地球科学学院,江苏徐州222116

出　　处：《水科学进展》2023年第1期12-20,共9页Advances in Water Science

基　　金：国家重点研发计划资助项目(2021YFC3000200;2018YFC0406500)。

摘　　要：海河流域天然河川径流持续衰减,水文丰枯情势显著变化,亟需研究适用于非一致性水文序列的丰枯概率计算方法。基于标准化径流指数、GAMLSS模型等方法,提出一种不同等级丰枯水事件期望发生次数和期望等待时间的计算方法,研究变化环境下海河流域天然河川径流丰枯概率的演变规律。结果表明:(1)径流丰枯概率呈现出显著的枯增丰减趋势;(2)同传统的一致性分布等多类概率分布相比,以时间t为协变量的LOGNO分布拟合流域径流系列的效果最优,且基于该分布计算的期望发生次数更接近于历史实际;(3)非一致性最优模型不同情景条件下计算的流域极端枯水和极端丰水事件的期望等待时间分别为4.9~9.4 a、14.5~36.0 a,说明海河流域近期发生极端枯水的概率远大于极端丰水。The natural river flow in the Haihe River basin has been continuously reducing in recent years, which leads to significant changes particularly in the wet and dry conditions of hydrology.As such, research on the calculation method of wet and dry probability catering for nonstationary hydrological series is needed.Based on the standardized runoff index and GAMLSS model, a new method computing expected number of occurrences(ENO) and expected waiting time(EWT) was proposed according to different levels of wet and dry hydrological events.Then, we further investigate on the evolution law of probability of wet and dry of natural river flow in Haihe River basin under changing environment.Our main finding are outlined as follows:(1) The probability of wet and dry of surface runoff showed a significant trend of increasing low flow and decreasing high flow.(2) Compared with other probability distributions including the traditional stationary hypothesis, the LOGNO distribution with time(t) as the covariate has the best performance when fitting the surface runoff series.The ENO of wet and dry hydrological events in the historical period calculated based on this distribution is closer to the actual situation.(3) The EWT of extremely dry and wet hydrological events in the Haihe River basin calculated under different scenarios of the nonstationary optimal model were 4.9—9.4 a and 14.5—36.0 a respectively, indicating that the considerably higher likelihood of extreme dry event in oncoming future.

关 键 词：河川径流 丰枯概率 非一致性 GAMLSS模型 海河流域 

分 类 号：P333.1[天文地球—水文科学]