摘要
河川径流和泥沙在塑造河流形态、提供水资源和维持区域环境及生态系统等方面起着重要作用。澜沧江-湄公河是亚洲著名的国际河流,近年来其上游澜沧江干流梯级电站开发引起的跨境水文、环境、生态和社会问题引起了国内外广泛关注。补远江作为澜沧江的主要支流之一,近年来随着澜沧江干流梯级电站的相继建成,其水文、生态效应愈发凸显,所以对该流域的过去几十年的水沙变化分析及其未来可能的演变趋势预测研究具有一定的科学与实际意义。本研究采用补远江下游曼安水文站50年的月平均流量序列,16年的月平均含沙量以及流域周边5个气象站49年的降水数据,分析该流域的水沙特征及演变趋势,并建立预报预测模型对流域径流、输沙进行中长期预报。研究得到以下主要认识：
(1)流域径流年际变化幅度不大,1960～2008年曼安站实测年径流总量的变差系数Cv为0.21,年际极值比K为2.82；流域输沙年际变化幅度较大,1993～2008年输沙量Cv值为0.59,年际极值比K达到7.1。流域水沙年内分配比较集中,都主要来源于汛期6-11月,其中径流年内分配不均匀系数为0.87,输沙年内分配不均匀系数高达1.65,说明流域水沙的季节性变化很大。Mann-Kendall趋势检验表明,流域年径流量和年悬移质输沙量均呈现一定的下降趋势。补远江单位流量的含沙量较小,曼安站1993～2008年年均来沙系数仅为0.006kg/(s.m6)。流量与含沙量呈现明显乘幂正相关,这一时段的月均流量与月均含沙量的水沙关系回归决定系数为0.6334,月输沙量与月径流量的水沙关系的回归决定系数达到0.8137,表明其拟合程度非常高。1993～2008年输沙量过程和径流过程与降水过程变化趋势基本一致,均呈现一定的下降趋势。1996～2008年期间,曼安站的大断面变化不大,所处的河床相对稳定。
(2)利用补远江曼安站水文资料,建立差分滑动自回归平均模型(ARIMA)对补远江汛期、枯水期的径流、泥沙序列进行建模预报。结果建立了ARMA (5,4)和ARMA(2,3)模型分别对补远江流域汛期、枯水期流量进行预测,得到2009～2013年枯水期平均流量为：316.01,174.81,288.02,342.24,283.97m3/s；汛期平均流量为：49.45,52.07,49.95,51.97,48.30m3/s；建立ARIMA(1,1,1)和ARIMA (1,1,2)模型分别对流域汛期、枯水期含沙量进行了预测,结果显示2009～2011年枯水期平均含沙量分别为：0.0179,0.0219,0.0263kg/m3；汛期平均含沙量分别为：0.8232,0.8029,0.7826kg/m3。
(3)基于Matlab的神经网络工具箱,建立经典的3层BP神经网络分别对补远江曼安水文站的年径流和年输沙量进行建模预测。其中对于径流最终建立了两输入神经元(年降水量和雨季降水量)、一个输出神经元(年径流量)和一个包含8个节点数的隐含层的经典3层BP神经网络预报模型；而对于流域年输沙量则建立了五个输入神经元(分别为汛期流量、年均流量、7、8、9三月均流量、年降水量和雨季降水量)、一个输出神经元(年输沙量)和一个包含7个节点数的隐含层的经典3层BP神经网络预报模型。总体上,两个模型拟合程度都很高,都达到了预期效果,拟合结果不管是定性,还是定量上都符合水文情报预报规范的要求。
Runoff and sediment, key factors in a river system, play an important role in shaping fluvial morphology and fluvial geomorphology. Besides, those supplying water resources and maintaining regional environmental and ecological systems. The Lancang-Mekong River is a famous international river in Asia. Recently, the development of cascade dams on the mainstream of Lancang River has aroused widespread concern at home and abroad on cross-border hydrological, environmental, ecological and social problems. Nowadays, most dams have been built, thereupon their hydrological and ecological effects began to highlight in the Buyuanjiang River, which is one of the main tributary in the lower part of the Lancang River. So further understanding on water and sediment changes in the basin over the past few decades and the future forecasting is necessary for science and practical application.
Used the lower reaches of Yangtze river basin the monthly average flow from1959-2008and monthly average sediment concentration from1993-2008at the Manan Gauging Station, and five meteorological stations rainfall data from1959-2007, this paper analyzed the runoff and sediment characteristics and evolution trend, then established the forecasting models for mid-long term hydrological forecasting.
Based on the data analysis and result deduction, detailed conclusions have been drawn as following:
(1) The inter-annual variability range of runoff was low during1960to2008, the coefficient of variation of annual runoff and inter-annual extreme value proportion at the Manan Gauging Station was0.21and2.72respectively; But from1993to2008, the inter-annual variability range of annual sediment load was high, and the variation coefficient of annual sediment load was0.59, and inter-annual extreme value proportion reach to7.1. The seasonal annual distribution of runoff and sediment was fairly concentrated, mainly comes from June to November within the flood season. And the intra-annual nonuniform coefficients of runoff and sediment load were high up to0.87and1.65. Therefore, seasonal changes of runoff and sediment were obvious.
The annual average incoming sediment coefficient is only0.006kg/(s.m6) at Manan Gauging Station from1993to2008, so the sediment concentration per unit flow of Buyuanjiang River was low. The average monthy flow and sediment concentration was significantly positive power function correlation during1993-2008; And during this period, the regression decision coefficient was0.6334, which indicated that the fitting degree was high; Compared with annual runoff and sediment load relationship, the regression decision coefficient of the relationship between month runoff and sediment load was0.8137, which indicated that the fitting degree was obvious. From1993to2008, the average annual sediment load and runoff process were showed as similar declining trends as precipitation process. During the period from1996to2008, changes in the Manan cross section is not obvious, showing relatively stable in riverbed morphology.
(2) Based on hydrological data at Manan hydrological station, this study built two autoregressive moving average models to forecast annual discharge and sediment concentration series. As a result, ARMA (5,4) and ARMA (2,3) regression model were elected to predict average flow of flood season and dry season respectively, the results showed dry season average flow would be49.4455,52.0674,49.9494,51.9686,48.2954m3/s and flood season average flow would be316.0015,174.8079,288.0196,342.2409,283.9677m3/s from2009-2013. And ARIMA (1,1,1), ARIMA (1,1,2) regression model were chose to predict mean sediment concentration of flood season and dry season respectively, the results indicated dry season mean sediment concentration would be0.0179,0.0219,0.0263kg/m3and flood season mean sediment concentration would be0.8232,0.8029,0.7826kg/m3during2009～2013.
(3) With Matlab's Neural Network Toolbox, two classic three-layer BP neural network models were built to simulate the annual runoff and sediment load respectively at Manan hydrological station. For Runoff forecasting model, chose annual rainfall and the rainy season precipitation as two input layer nodes. The number of hidden layer neurons, which selected base on the empirical formula and used the "trial and error" method, according to the fitting results, was set to8in the end. The output layer has one neuron:annual runoff. Sediment forecasting model ultimately preferred a classic three-layer BP network which contain the five inputs:flood seasom flow, annual flow, average monthly flow from July to September, annual precipition and Rainy season precipitation; one output:annual sediment load; and one hidden layer which contained7nodes. Overall, the fitting degrees of those two models are very high, and achieved the desired effect. The fitting results, whether qualitative or quantitative, are in accordance with requirements of "Standard for hydrological information and hydrological forecasting".
引文
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