黄河头道拐断面水沙变化特征及其影响因素分析  

Characteristics of runoff and sediment changes in the Toudaoguai section of the Yellow River and analysis of its influence factors

作　　者：郭晶 赵水霞 李超[1] 崔盛杰 梁雅祺 GUO Jing;ZHAO Shui-xia;LI Chao;CUI Sheng-jie;LIANG Ya-qi(College of Water Conservancy and Civil Engineering,Inner Mongolia Agricultural University,Hohhot 010018,China;Institute of Water Resources in Pastoral Area,Ministry of Water Resources,Hohhot 010020,China)

机构地区：[1]内蒙古农业大学水利与土木建筑工程学院,内蒙古呼和浩特0010018 [2]水利部牧区水利科学研究所,内蒙古呼和浩特宇010020

出　　处：《泥沙研究》2025年第1期47-54,60,共9页Journal of Sediment Research

基　　金：中国水利水电科学研究院流域水循环模拟与调控国家重点实验室开放基金项目(IWHR-SKL-KF202113);国家自然科学基金项目(51969025,52009084)。

摘　　要：径流量和输沙量是河道水资源开发利用与保护重点考虑的因素。利用1960—2021年黄河上中游头道拐水文站年水沙资料,分析了该站的水沙变化过程及影响因素,并对比分析了输沙量适宜的预报方法。结果表明:61年来,头道拐站的径流量和输沙量均整体呈明显减少的趋势,2006—2021年径流量和输沙量呈现先减小后增加的特征,典型突变年份为2017年;水沙年内分配表现为冰期径流量为畅流期的37%,冰期输沙量仅为畅流期的13.7%;影响该站径流量和输沙量的主要因素为上游水库下泄流量,其与径流量和输沙量的相关系数分别为0.99和0.93;经实测数据验证,BP神经网络输沙量预测模型精度显著高于RBF神经网络,相对误差仅为8.16%。Runoff and sediment discharge are the key factors to be considered in the development,utilizand protection of river water resources.Based on the annual runoff and sediment data at Toudaoguai hydrological station in the upper and middle reach of the Yellow River from 1960 to 2021,the variation process and influencing factors of runoff and sediment in the station from 2006 to 2021 were studied,and the suitable forecasting methods of sediment transport were compared and analyzed.The results show that:in the recent 60 years,the runoff and sediment discharge at Toudaoguai station have a significant decreasing trend as a whole,and show the characteristics of decreasing first and then increasing from 2006 to 2021,with the typical mutation year of 2017.The runoff and sediment transport during the ice cover period are 37%and 13%of those during the free flow period,respectively.The main factors affecting the runoff and sediment discharge at the station is the flowrate discharging from the upstream reservoir,with the positive correlation coefficients of 0.99 and 0.93 respectively.By comparing with the measured data,the BP neural network has higher accuracy than the RBF neural network for predicting sediment transport,with the relative error of only 8.16%.

关 键 词：头道拐水文站 冰期 畅流期 下泄流量 BP神经网络 

分 类 号：TV882.1[水利工程—水利水电工程]