【摘要】：水庫(kù)的修建顯著改變了進(jìn)入下游河道的水沙條件,對(duì)下游河道演變也具有顯著影響。小浪底水庫(kù)是黃河干流最靠近下游的一座水庫(kù),控制著進(jìn)入黃河下游河道的水沙過程。經(jīng)過多年的初期運(yùn)用,小浪底水庫(kù)在減少水庫(kù)下游河道淤積和穩(wěn)定合適的中水河槽規(guī)模發(fā)揮了重要作用。本文以小浪底水庫(kù)為例,分析了小浪底水庫(kù)初期運(yùn)用以來(lái)黃河下游水沙變化及河道沖淤演變過程,并研究了下游河道演變與水沙過程的響應(yīng)。本文主要研究成果總結(jié)如下:(1)小浪底水庫(kù)運(yùn)用后15年內(nèi),下游水量年內(nèi)分配發(fā)生明顯改變,汛期來(lái)水量比重明顯小于非汛期,下泄沙量明顯減少。調(diào)水調(diào)沙期間,水庫(kù)最大下泄流量形成平頭洪峰,中水流量持續(xù)的時(shí)間增加;水沙趨勢(shì)分析表明,黃河下游來(lái)水來(lái)沙量在1950～2015近65年整體呈顯著減小趨勢(shì),小浪底水庫(kù)運(yùn)用后,年徑流量變化趨勢(shì)趨于穩(wěn)定,年輸沙量呈顯著降低趨勢(shì);通過水沙多時(shí)間尺度分析,花園口站年徑流量、輸沙量變化分別以22a和9a尺度為首要主周期。黃河下游花園口站水沙序列的周期規(guī)律與中游潼關(guān)站基本一致,小浪底水庫(kù)(聯(lián)合三門峽水庫(kù))運(yùn)用對(duì)下游水沙序列多時(shí)間尺度特性影響不大。(2)統(tǒng)計(jì)分析了小浪底水庫(kù)運(yùn)用及調(diào)水調(diào)沙期間黃河下游河道沖淤特點(diǎn),分析了河道橫斷面形態(tài)調(diào)整及斷面特征參數(shù)、平灘流量的演變過程。結(jié)果表明:小浪底水庫(kù)運(yùn)用后黃河下游河道均保持相對(duì)穩(wěn)定的沖刷。18次調(diào)水調(diào)沙沖刷量?jī)H占下游總沖刷量的14.69%,不是下游沖刷的主體,2007年后調(diào)水調(diào)沙沖刷效率逐漸降低;小浪底水庫(kù)運(yùn)用后,各典型河段橫斷面均以沖刷下切為主,局部河段伴有橫向展寬;小浪底水庫(kù)運(yùn)用后,平灘流量有一定回升,2007年底黃河下游四站均值已恢復(fù)到了 4000m3/s以上。(3)運(yùn)用回歸分析(含逐步回歸)、BP神經(jīng)網(wǎng)絡(luò)研究了黃河下游平灘面積、平灘流量與水沙條件的響應(yīng)關(guān)系。結(jié)果表明:平灘面積、平灘流量受前期水沙因子的影響時(shí)間大致為4～6年。考慮流量和來(lái)沙系數(shù)兩個(gè)主要因子,以5年滑動(dòng)平均值擬合出平灘流量與兩個(gè)因子的相關(guān)性公式,基本能反映平灘流量隨水沙條件的年際變化過程;考慮平灘流量的多個(gè)影響因子,基于逐步回歸方法和BP神經(jīng)網(wǎng)絡(luò)模型建立了平灘流量的預(yù)測(cè)模型,就總體預(yù)測(cè)精度而言,BP神經(jīng)網(wǎng)絡(luò)逐步回歸模型汛期水沙擬合模型,但BPyL經(jīng)網(wǎng)絡(luò)對(duì)于平灘流量極值點(diǎn)的預(yù)測(cè)效果較差,需要更大范圍數(shù)據(jù)的訓(xùn)練。
[Abstract]:The construction of reservoir has changed the condition of water and sediment entering the downstream channel and has a significant influence on the evolution of the downstream channel. Xiaolangdi Reservoir is a reservoir closest to the lower reaches of the Yellow River, which controls the process of water and sediment entering the lower Yellow River. After many years of initial application, Xiaolangdi Reservoir plays an important role in reducing the deposition of the lower reaches of the reservoir and stabilizing the appropriate scale of the middle water channel. Taking Xiaolangdi Reservoir as an example, this paper analyzes the variation of water and sediment in the lower reaches of the Yellow River and the evolution process of channel erosion and siltation since the initial operation of Xiaolangdi Reservoir, and studies the response of the evolution of the lower reaches of the river and the process of water and sediment. The main research results of this paper are summarized as follows: (1) within 15 years after the operation of Xiaolangdi Reservoir, the annual distribution of downstream water volume has changed obviously, the proportion of incoming water in flood season is obviously smaller than that in non-flood season, and the amount of sediment discharge is obviously reduced. During the period of water and sediment regulation, the maximum discharge of the reservoir formed flat head Hong Feng, and the duration of the middle water flow increased. The analysis of water and sediment trend shows that the amount of incoming water and sediment from the lower reaches of the Yellow River decreased significantly in the last 65 years from 1950 to 2015. After the operation of Xiaolangdi Reservoir, the change trend of annual runoff tended to be stable and the annual sediment discharge decreased significantly. Based on the analysis of multi-time scale of water and sediment, the annual runoff and sediment discharge of Huayuankou Station are mainly controlled by the scale of 22a and 9a, respectively. The periodicity of water and sediment sequence of Huayuankou station in the lower reaches of the Yellow River is basically the same as that of Tongguan station in the middle reaches of the Yellow River. The use of Xiaolangdi Reservoir (United Sanmenxia Reservoir) has little effect on the multi-time scale characteristics of the downstream water and sediment sequence. (2) the characteristics of the channel scouring and silting in the lower Yellow River during the operation and the water and sediment transfer of the Xiaolangdi Reservoir are analyzed statistically. The course of river channel cross section shape adjustment, section characteristic parameter and flat discharge evolution is analyzed. The results show that after the operation of Xiaolangdi Reservoir, the channel of the lower reaches of the Yellow River keeps relatively stable scour, and the amount of 18 times of water and sediment transfer only accounts for 14.69% of the total amount of the downstream scour, which is not the main part of the downstream scour. After 2007, the efficiency of water and sediment transfer and erosion gradually decreased; After the operation of Xiaolangdi Reservoir, the cross section of each typical reach is mainly cut down by scouring, and the local reach is accompanied by transverse widening. After the operation of Xiaolangdi Reservoir, the flat discharge has recovered to a certain extent, and the mean value of the four stations in the lower Yellow River has recovered above 4000m3/s at the end of 2007. (3) the flat area of the lower Yellow River has been studied by means of regression analysis (including stepwise regression), BP neural network). Response relationship between flat discharge and water and sediment conditions. The results show that the area and discharge of flat beach are affected by the factors of water and sediment in the early stage, and the influence time is about 4 ~ 6 years. Considering the two main factors of discharge and sediment supply coefficient, the correlation formula of flat beach discharge and two factors is fitted by the 5-year sliding average value, which can basically reflect the interannual variation process of flat beach discharge with water and sediment conditions. Based on the stepwise regression method and the BP neural network model, the prediction model of flat beach discharge is established considering several influencing factors of flat flow. In terms of the overall prediction accuracy, the BP neural network stepwise regression model is used to fit the water and sediment in flood season. However, the prediction effect of BPyL network for the maximum point of flat flow is poor, and the training of larger range of data is needed.
【學(xué)位授予單位】：中國(guó)水利水電科學(xué)研究院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TV147

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