澜沧江自然条件下输沙质量通量与体积径流量的关系
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摘要
为了研究澜沧江水沙关系和提高利用体积径流量预测泥沙质量通量的准确性,以电站建设前1982—2000年澜沧江上游旧州站和中游戛旧站沙的质量浓度和体积径流量实测资料为研究对象,根据自然状态下泥沙质量通量和体积径流量变化幅度的差异,建立输沙质量通量导数与体积径流量的关系式.公式系数为流域系统属性的综合反映,表示平均体积径流量下输沙质量通量的变化率,公式指数表示体积径流量变化对输沙质量通量变化率的影响.根据上中游站的指数差异可知,泥沙质量通量除依赖上游来沙外还依赖沿程冲刷和支流入汇的补给.通过对输沙质量通量导数公式进行积分得出输沙质量通量与体积径流量的理论关系.结果表明,输沙质量通量峰值约落后体积径流量峰值1 d,据此优化反向传播神经网络(BP-NN),可以较好地改良优化前模型预测中的峰值偏移现象,提高预测精度.
The relationship between sediment mass flux derivative and volume runoff was developed according to the difference in rangeability of sediment mass flux and volume runoff under natural condition. Based on the measured data of sediment mass concentration and volume runoff of the upriver Jiuzhou station and the middleriver Gajiu station of Lancang River from 1982 to 2000 before the construction of the power station, this work studied the relation between water and sediment in Lancang River and improved the accuracy of using the volume runoff to predict the sediment mass flux. The coefficient of formula comprehensively reflectes the property of the basin system and represents the change rate of sediment mass flux at mean volume runoff. The index of formula reflectes the influence of volume runoff change on the change rate of sediment mass flux. The difference of indexes between upriver and middleriver stations shows that the sediment mass flux not only depends on the upstream sediment, but also on the alongshore erosion and the recharge of tributaries. The theoretical relationship between sediment mass flux and volume runoff is obtained by the integration of derivative formula of sediment mass flux. Results show that the peak of volume runoff appeares one day before the peak of sediment mass flux, based on which the backpropagation neural network(BP-NN) can be optimized. The optimized method can be used to better improve the peak shift phenomenon predicted by the original model and improve the prediction accuracy.
The relationship between sediment mass flux derivative and volume runoff was developed according to the difference in rangeability of sediment mass flux and volume runoff under natural condition. Based on the measured data of sediment mass concentration and volume runoff of the upriver Jiuzhou station and the middleriver Gajiu station of Lancang River from 1982 to 2000 before the construction of the power station, this work studied the relation between water and sediment in Lancang River and improved the accuracy of using the volume runoff to predict the sediment mass flux. The coefficient of formula comprehensively reflectes the property of the basin system and represents the change rate of sediment mass flux at mean volume runoff. The index of formula reflectes the influence of volume runoff change on the change rate of sediment mass flux. The difference of indexes between upriver and middleriver stations shows that the sediment mass flux not only depends on the upstream sediment, but also on the alongshore erosion and the recharge of tributaries. The theoretical relationship between sediment mass flux and volume runoff is obtained by the integration of derivative formula of sediment mass flux. Results show that the peak of volume runoff appeares one day before the peak of sediment mass flux, based on which the backpropagation neural network(BP-NN) can be optimized. The optimized method can be used to better improve the peak shift phenomenon predicted by the original model and improve the prediction accuracy.
引文
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[2]刘成,王兆印,隋觉义.我国主要入海河流水沙变化分析[J].水利学报,2007(12):1444-1452.LIU Cheng,WANG Zhao-yin,SUI Jue-yi.Analysis on variation of seagoing water and sediment load in main rivers of China[J].Journal of Hydraulic Engineering,2007(12):1444-1452.
[3]张信宝,文安邦.长江上游干流和支流河流泥沙近期变化及其原因[J].水利学报,2002(4):56-59.ZHANG Xin-bao,WEN An-bang.Variations of sediment in upper stream of Yangtze River and itstributary[J].Journal of Hydraulic Engineering,2002(4):56-59.
[4]DAI Z J,FAGHERAZZI S,MEI X F,et al.Decline in suspended sediment concentration delivered by the Changjiang(Yangtze)River into the East China Sea between 1956 and 2013[J].Geomorphology,2016(268):123-132.
[5]ZHAO G J,MU X M,SU B D,et al.Analysis of stream flow and sediment flux changes in the Yangtze River basin[J].Water International,2012,37(5):537-551.
[6]FAN H M,CAI Q G,GUO C J,et al.Temporal and spatial dynamics of different particle size of suspended sediment in the Liu River catchments,China[J].International Journal of Sediment Research,2006(3):209-219.
[7]马睿,马良,张罗号,等.黄河流域典型沙质河段冲淤量预估方法及应用[J].水利学报,2016,47(10):1277-1286.MA Rui,MA Liang,ZHANG Luo-hao,et al.Prediction method for erosion and deposition on typical sandy bed sections of the Yellow River and its applications[J].Journal of Hydraulic Engineering,2016,47(10):1277-1286.
[8]FU K D,WANG C,SU B,et al.Distribution and pollution assessment of heavy metals in sand sediment of reservoir along the middle and lower reaches of Lancang River[J].Ecological Economy,2016,32(12):163-168.
[9]刘成,王建军,何耘.澜沧江-湄公河输沙量变化及其影响因素[J].水利水电科技进展,2013,33(1):7-12.LIU Cheng,WANG Jian-jun,HE Yun.Change in sediment loads in the Lancang-Mekong River and its influencing factors[J].Advances in Science and Technology of Water Resources,2013,33(1):7-12.
[10]周安辉.澜沧江近20年泥沙变化特性分析[J].人民长江,2012,43(增1):114-115.ZHOU An-hui.Analysis of sediment change characteristics of Lancang River in recent 20 years[J].Yangtze River,2012,43(Suppl.1):114-115.
[11]ZHONG H P,WANG J S.Impacts from hydropower development of main stream on runoff of Lancangjiang River[J].Water Resources and Hydropower Engineering,2010,41(12):72-74.
[12]LI H C,WANG G Q,HAO Z C,et al.Characteristics analysis of hydrometeorological elements in Lancang River basin[J].Journal of Water Resources and Water Engineering,2017,28(4):21-27.
[13]ZHAI H J,HU B,LUO X Y,et al.Spatial and temporal changes in runoff and sediment loads of the Lancang River over the last 50 years[J].Agricultural Water Management,2016(174):74-81.
[14]张瑞瑾,谢鉴衡,陈文彪.河流动力学[M].武汉:武汉大学出版社,2007,414.
[15]WARNER J C,GEYER W R,LERCZAK J A.Numerical modeling of an estuary:a comprehensive skill assessment[J].Journal of Geophysical Research,2005,110:C05001.
[16]LI B,LI L J,LI H B,et al.Changes in precipitation extremes in Lancang River Basin,1965-2005[J].Progress in Geography,2011,30(3):290-298.
[17]RUMELHART D E,HITON G E,WILLIAMS R J.Parallel distributed processing:explorations in the microstructure of cognition[M].Cambridge:MIT Press,1986.
[18]齐银峰,谭荣建.基于改进粒子群优化算法的BP神经网络在大坝变形分析中的应用[J].水利水电技术,2017,48(2):118-124.QI Yin-feng,TAN Rong-jian.Application of improved particle swarm optimization algorithmbased BP neural network to dam deformation analysis[J].Water Resources and Hydropower Engineering,2017,48(2):118-124.
[19]赵文喜,周滨,刘红磊,等.基于BP神经网络的海河干流叶绿素浓度短时预测研究[J].水利水电技术,2017,48(11):134-140.ZHAO Wen-xi,ZHOU Bin,LIU Hong-lei,et al.BPneural network-based short-term prediction of chlorophyll concentration in main stream of Haihe River[J].Water Resources and Hydropower Engineering,2017,48(11):134-140.