【摘要】：小流域的水文站點(diǎn)分布密度低,常常缺乏足夠的水文信息和數(shù)據(jù)。有些小流域甚至缺乏基礎(chǔ)雨量資料,但小流域洪水應(yīng)用極為廣泛且影響計(jì)算結(jié)果的經(jīng)驗(yàn)參數(shù)較多,所以大部分小流域洪水計(jì)算公式都是經(jīng)過一系列的假定和模型概化而建立的,這樣使得計(jì)算參數(shù)大幅減少,同時(shí)又不至于計(jì)算結(jié)果偏差太大。雖然計(jì)算參數(shù)的大幅減少使計(jì)算方法更為簡(jiǎn)便,但僅有的幾個(gè)計(jì)算參數(shù)對(duì)計(jì)算結(jié)果的影響也必然隨之增大。 本文介紹了兩種常用的小流域洪水洪峰流量的計(jì)算方法,即水科院推理公式法和林平一法,并利用EHP軟件各進(jìn)行了三個(gè)實(shí)際算例的計(jì)算。通過對(duì)兩種方法的主要計(jì)算參數(shù)的分析,篩選出不確定性較大的參數(shù),對(duì)篩選出的參數(shù)進(jìn)行±0.5%、±1%、±2%、±5%、±10%、±15%和±20%等7個(gè)不同幅度的不重復(fù)擾動(dòng),來計(jì)算參數(shù)的靈敏度系數(shù)大小。通過靈敏度系數(shù)的比較,即可得知各參數(shù)具有的誤差對(duì)洪峰流量計(jì)算成果精度的影響程度。文中不僅分別對(duì)水科院推理公式法和林平一法中各自的參數(shù)進(jìn)行了靈敏度分析,還對(duì)兩種方法中涉及的共性參數(shù)進(jìn)行了對(duì)比分析,以便得出其一般性的趨勢(shì)。取得的主要研究結(jié)果如下： 1.推理公式法中損失參數(shù)μ、匯流參數(shù)m、河道縱比降J和24h最大降雨量H24等四個(gè)參數(shù)的靈敏度系數(shù)的絕對(duì)值大小依次為其中|QJ|和|Qμ|兩個(gè)較小,均小于0.5,表明損失參數(shù)μ和河道縱比降J兩個(gè)參數(shù)的靈敏度系數(shù)較小,對(duì)洪峰流量計(jì)算成果精度影響較��；其次,影響較大的參數(shù)為匯流參數(shù)m,|Qm|主要分布在0.8～1.2之間,對(duì)洪峰流量的計(jì)算成果影響較大；影響最大的為|QH24h|,并且大于1,表明在上述四個(gè)參數(shù)中24h最大降雨量H24靈敏度系數(shù)最大。另外,各參數(shù)在5%范圍以內(nèi)變化時(shí),其靈敏度系數(shù)隨著參數(shù)的擾動(dòng)的波動(dòng)較大。 2.林平一法中穩(wěn)定下滲率μ和河道坡度J。兩個(gè)參數(shù)的靈敏度系數(shù)的絕對(duì)值|q|和|OJc|較小,其絕對(duì)值均小于0.5,表明穩(wěn)定下滲率μ和河道坡度Jc兩個(gè)參數(shù)對(duì)洪峰流量計(jì)算成果影響較�。缓拥绤R流糙率Nc的靈敏度系數(shù)的絕對(duì)值|QNc|一般小于1,但接近于1,表明河道匯流糙率Nc對(duì)洪峰流量計(jì)算成果影響較大；其中靈敏度最大的參數(shù)為24h最大降雨量H24,其靈敏度系數(shù)的絕對(duì)值|QH24h|大于1,表明在上述四個(gè)參數(shù)中24h最大降雨量H24靈敏度系數(shù)最大。另外,各參數(shù)在5%范圍以內(nèi)變化時(shí),其靈敏度系數(shù)隨著參數(shù)的擾動(dòng)的波動(dòng)較大。 3.推理公式法和林平一法兩種方法的共性參數(shù)主要有J（c）和H24h。參數(shù)J（c）的靈敏度系數(shù)0QJ（c）1，表明兩種方法中的J（c）與其靈敏度系數(shù)QJ（c）均呈正相關(guān)，并且參數(shù)J（c）的變化對(duì)洪峰流量產(chǎn)生的誤差起到縮小作用；隨著參數(shù)J（c）的增大，QJ（c）一般呈減小趨勢(shì)；相比較而言，參數(shù)J（c）的靈敏度系數(shù)QJ（c）在推理公式法中比林平一法中更大一些。參數(shù)H24h的靈敏度系數(shù)QH241，，表明兩種方法中的H24h與其靈敏度系數(shù)QH24均呈正相關(guān)，并且參數(shù)H24h的變化對(duì)洪峰流量產(chǎn)生的誤差起到擴(kuò)大作用；隨著參數(shù)H24h的增大，QH24一般呈增大趨勢(shì)；參數(shù)H24h的其靈敏度系數(shù)QH24的大小關(guān)系在推理公式法中和林平一法中無明顯的比較規(guī)律。
[Abstract]:The distribution density of hydrological stations in small basins is low and often lacks sufficient hydrological information and data. Some small basins even lack the basic rainfall data, but the flood application of small watershed is very extensive and the empirical parameters affecting the calculation results are many, so most of the flood calculation public formulas in most small basins are generalized by a series of assumptions and model generalizability. In this way, the calculation parameters are greatly reduced and the deviation of the calculation results is not too large. Although the large reduction of the calculation parameters makes the calculation more convenient, the only several calculation parameters will inevitably increase the effect of the calculation results.
This paper introduces two methods for calculating flood and flood peak flow of small watersheds, namely, the method of reasoning formula of the Academy of water science and the Lin Ping one method, and the calculation of three practical examples using EHP software. Through the analysis of the main calculation parameters of the two methods, the uncertain parameters are screened out, and the selected parameters are 0.5% and +. 1%, + 2%, + 5%, + 10%, + 15% and + 20% and other 7 different amplitude non repetitive disturbances, to calculate the sensitivity coefficient of the parameters. Through the comparison of the sensitivity coefficient, we can know the degree of influence of the error on the accuracy of the calculation results of the flood peak flow. The sensitivity analysis of the parameters is carried out, and the common parameters involved in the two methods are compared and analyzed in order to get the general trend. The main results obtained are as follows:
1. the absolute value of the sensitivity coefficients of four parameters, such as the loss parameter mu, the confluence parameter m, the channel longitudinal ratio drop J and the 24h maximum rainfall H24, are respectively |QJ| and |Q Mu two smaller, both less than 0.5, indicating that the loss parameter mu and the channel longitudinal ratio drop J two parameters are smaller, and the calculation results of the flood peak flow are refined. Second, the parameter of large influence is m, and |Qm| is mainly distributed between 0.8 and 1.2, which has great influence on the calculation results of peak flow; the maximum impact is |QH24h|, and more than 1, which indicates that the maximum 24h precipitation sensitivity coefficient of the maximum rainfall is maximum in the above four parameters. In addition, when the parameters vary within the range of 5%, The sensitivity coefficient fluctuates with the perturbation of the parameters.
2. the absolute value |q| and |OJc| of the sensitivity coefficient of the stable infiltration rate and the channel gradient J. two parameters are smaller than 0.5. It shows that the steady infiltration rate Mu and the channel slope Jc two parameters have little influence on the calculation results of the flood peak flow, and the absolute value of the sensitivity coefficient of the channel flow roughness Nc is generally less than 1, But close to 1, it shows that the river flow roughness Nc has great influence on the calculation results of the flood peak flow. The maximum sensitivity parameter is 24h maximum rainfall H24, and the absolute value of the sensitivity coefficient is more than 1, indicating that the maximum 24h rainfall H24 sensitivity coefficient is maximum in the four parameters of the above four parameters. In addition, when each parameter varies within 5% range, The sensitivity coefficient fluctuates with the perturbation of the parameters.
3. the common parameters of 3. reasoning formula method and two methods are mainly J (c) and H24h. parameter J (c) sensitivity coefficient 0QJ (c) 1. It shows that J (c) in the two methods is positively correlated with the sensitivity coefficient QJ (c), and the variation of the parameter J is reduced to the error of the peak flow rate. In comparison, the sensitivity coefficient QJ (c) of parameter J (c) is larger in the reasoning formula method than in the Linping method. The sensitivity coefficient QH241 of the parameter H24h shows that the H24h of the two methods is positively correlated with the sensitivity coefficient QH24, and the variation of the parameter H24h plays an important role in the error of the peak flow. With the increase of parameter H24h, QH24 generally assumes an increasing trend, and the relation between the sensitivity coefficient QH24 of parameter H24h has no obvious comparative law in the inference formula method and Lin Ping one method.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TV122

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