學(xué)位論文-—水庫(kù)泥沙淤積的不確定因素分析外文翻譯

1、外文翻譯Uncertainty Analysis Of ReservoirSedimentationAbstract : Significant advances have been made in understanding the importance of the factors involved in reservoir sedimentation. However, predicting the accumulation of sediment in a reservoir is still a complex problem. In estimating reservoir sed

2、imentation and accumulation, a number of uncertainties arise. These are related to quantity of streamflow, sediment load, sediment particle size, and specific weight, trap efficiency, and reservoir operation。 In this study, Monte Carlo simulation and Latin hypercube sampling are used to quantify the

3、 uncertainty of annual reservoir sedimentation and accumulated reservoir sedimentation through time. In addition, sensitivity analysis was performed to examine the importance of various factors on the uncertainty of annual reservoir sedimentation. The proposed procedures have been applied to the Ken

4、ny Reservoir at the White River Basin in Colorado.The uncertainty of annual reservoir sedimentation and the effect of each uncertain factor, taken individually and in combinations, on the uncertainty of accumulated reservoir sedimentation through time have been examined. The results show that annual

5、 streamflow and sediment load are the most important factors determining the variability of annual reservoir sedimentation and accumulated reservoir sedimentation.In the case of Kenny Reservoir, the uncertainty expressed by the coefficient of variation can be on the order of 65% for annual reservoir

6、 sedimentation and 39% for accumulated reservoir sedimentation volume.IntroductionReservoir sedimentation varies with several factors such as sediment production, sediment transportation rate, sediment type, mode of sediment deposition, reservoir operation, reservoir geometry, and streamflow variabi

7、lity. Sediment is transported as suspended and bed loads by streams and rivers coming into a reservoir. Due to flow deceleration when a river approachesa reservoir, the sediment transport capacity decreases,andsome of the incoming sediment is trapped and deposited in the reservoir. In addition, the

8、deposited sediments may consolidate by their weight and the weight of overlying water through time. Predicting the sediment coming into a reservoir,its deposition, and its accumulation throughout the years, after construction of the dam, have been important problems in hydraulic engineering. Despite

9、 the advances made in understanding several of the factors involved in reservoir sedimentation, predicting the accumulation of sediment in a reservoir is still a complex problem. Empirical models, based on surveys and field observations, have been developed and applied to estimate annual reservoir s

10、edimentation load (RSL), accumulated reservoir sedimentation load, (ARSL), and accumulated reservoir sedimentation volume (ARSV) after a given number of years of reservoir operation. Likewise, several mathematical models for predicting reservoir sedimentation have been developed based on the equatio

11、ns of motion and continuity for water and sediment.However,empirical methods are still widely used in actual engineering practice.In estimating resevoir sediment inflow, reservoir sedimentation,and reservoir sediment accumulation, either by empirical or analytical approaches, a number of uncertainti

12、es arises.The main factors affecting reservoir sedimentation are (1)quantity of streamflow; (2) quantity of sediment inflow into a reservoir;(3) sediment particle size; (4) specific weight of the deposits; and (5) reservoir size and operation. Depending on the particular case at hand, some factors m

13、ay be more important than others. All of these factors are uncertain to some degree and, as a consequence, reservoir sedimentation will be an uncertain quantity too.In addition, which model (or procedure) is applicable to estimate some of the foregoing quantities and, in fact, which model is to be u

14、sed to estimate the amount of sediment that will be trapped in a reservoir are questions that cannot be answered with certainty. For instance, Fan (1988) obtained information on 34 stream-,18 watershed-, and 20 reservoir-sedimentation models and stated that different models may give significantly di

15、fferent results even when using the same set of input data. Such an additional factor, known as moduenlcertainty, maybe quite a large component of the overall uncertainty. In any case, the planner and manager of a reservoir may be interested in quantifying how the uncertainty of some of the factors

16、affecting reservoir sedimentation translate into the uncertainty of annual sediment deposition and accumulated sediment deposition through time.In thispaper, we addressthe issue quantifying the effect of parameter uncertainty on reservoir sedimentation based on a set of predefined models as will be

17、described below.The effect of model uncertainty is not considered in this study.Several methods of uncertainty analysis have been developed and applied in water resources engineering. The most widely used methods are first-order analysis (FOA) and Monte Carlo simulation (MCS). FOA is based on linear

18、izing the functional relationship that relates a dependent random variable and a set of independent random variables by Taylor series expansion. This method has been applied in several water resources and environmental engineering problems involving uncertainty. Examples include storm sewer design;

19、ground-water-flow estimation , prediction of dissolved oxygen;and subsurface-flow and contaminant transport estimation . In MCS, stochastic inputs are generated from their probability distributions and are then entered into empirical or analytical models of the underlying physical process involved i

20、n generating stochastic outputs. Then, the generated outputs are analyzed statistically to quantify the uncertainty of the output. Many examples of uncertainty analysis by MCS can be found in water resources and environmental engineering. Some examples include steady-state ground-water-flow estimati

21、on and water-quality modeling . Scavia et al. (1981) made a comparison of MCS and FOA for determining uncertainties associated with eutrophication model outputs such as phytoplankton, zooplankton, and nitrogen forms.They indicated that both MCS and FOA agree well in estimating the mean and variance

22、of model estimates.However, MCS has the advantage of providing better information about the output frequency distribution.Latin hypercube sampling (LHS) is an alternative simulation procedure that has been developed for uncertainty analysis of physical and engineering systems.The basic idea behind L

23、HS is to generate random stochastic inputs in a stratified manner from the probability distributions. In this way the number of generated inputs can be reduced considerably as compared to MCS.They pointed out that the point estimate method yields a larger mean and variance than those obtained by the

24、 FOA and LHS methods. Furthermore, in studying the importance of stochastic inputs on the output by sensitivity analysis, LHS yields more information than the other two methods.In this study, uncertainty analysis based on MCS and LHS methods are conducted to estimate the probability distribution of

25、annual reservoir sedimentation volume (RSV). In addition,sensitivity analysis is performed to see the relative importance of stochastic inputs in estimating the variability of RSV. Furthermore,uncertainty analysis of ARSV throughout time is performed using MCS.In this procedure, annual streamflows a

26、re generated by a stochastic time series model. The effect of parameter uncertainty in the stochastic model on the output (i.e.,ARSV) is also considered.Estimation Of Annual And AccumulatedReservoir Sediment Load(Mass) And VolumeReservoir sedimentation volume depends, among other factors,on the quan

27、tity of sediment inflow, the percentage of sediment inflow trapped by the reservoir, and the specific weight of the deposited sediment considering the effect of compaction with time.The incoming sediment load and the streamflow discharge are usually measured at hydrometric gauging stations, and a se

28、diment rating curve is constructed.The sediment rating curve expresses the relationship between the rate of sediment discharge and the rate of streamflow discharge and is usually represented graphically on logarithmic coordinates.Incoming sediment is generally composed of suspended sediment and bed

29、load. When the bed load cannot be obtained by measurements, it can be estimated by formulas.In estimating annual sediment load, it has been common practice to use annual sediment rating curves for both suspended sediment and bed load. The annual sediment rating curve is the relation between annual s

30、ediment load and annual streamflow discharge.Two methods can be considered for determining annual sediment rating curves . A simple method involves the following steps: (1) For a given year calculate daily sediment loads from daily sediment rating curves; (2) add all daily sediment loads and divide

31、the sum by the number of days in the year, then this value represents the annual average sediment load in tons per day; (3) repeat Steps 1 and 2 for all years of record; and (4) plot the annual average sediment load versus the annual average streamflow for each year in the record. An alternative met

32、hod is based on estimating annual sediment loads using flow duration curves. In any case, an annual sediment rating curve can be constructed by simple regression analysis after logarithmic transformation of annual average streamflow discharges and annual average sedime nt loads. Colby (1956) stated

33、that in actual practice daily sedime nt rat ing curves could be assumed to be equivale nt to in sta ntan eous sedime nt rat ing curves.Daily rating curves of suspended sediment and bed load may be represe nted aslog10 QSD = log10 Cs a； h logg QWD(1)log10 QSD 二 log；o 4 2 0 log；。QWD(2)where QSD = dail

34、y suspe nded sedime nt load (ton s/day); QBD= daily bedIload (tons/day); QWD = daily average streamflow discharge (m3/s);and a；, b； and a2, b2 = rati ng curve coefficie nts for suspe ndedsedime nt and bed load, respectively. Cs is a rati ng curve correcti on factor n eeded to avoid underestimating t

35、he estimate of suspended load.Such a correction factor Cs is equal to exp(2.65s ) where s is the residual standard error (Ferguson 1986). Likewise, Cb is the correction factor for bed load. The corresponding annual rati ng curves of suspe nded sedime nt and bed load arelog；oQSt =a； b; log；QWt(3)log；

36、o QBt =a2 b2 log；。Qg(4)where QSt = (annual average) suspe nded sedime nt load (ton s/day) in year QBt = (annual average) bed load (ton s/day) in year t; QWt = (annual average) streamflow discharge (m3/s) in year t; and a, b and a2, p = rati ng curve coefficients for annual average suspended-sediment

37、 and bed loads, respectively.Then, the (average) total sediment inflow in year t, QTt, is simply QTt = QSt + QBt.譯文水庫(kù)泥沙淤積的不確定因素分析摘要： 盡管在理解關(guān)于水庫(kù)中所涉及的幾個(gè)因素的重要性方面取得了很大的進(jìn) 展，然而，預(yù)測(cè)水庫(kù)泥沙的淤積仍然是一個(gè)復(fù)雜的問(wèn)題。 水庫(kù)泥沙的沉淀和淤積， 出現(xiàn)了一些不確定因素。這些涉及到流量，泥沙，泥沙顆粒大小，比重，攔沙效 率和水庫(kù)運(yùn)行等。在這項(xiàng)研究中，蒙特卡洛模擬法和拉丁超立方抽樣法是用來(lái) 量化水庫(kù)年度泥沙淤積和水庫(kù)泥沙淤積過(guò)程的不確定性。

38、此外，通過(guò)敏感度分析 來(lái)確定水庫(kù)年度泥沙淤積各種不確定性因素的重要性。 這個(gè)程序已經(jīng)應(yīng)用到科羅 拉多州白河流域的肯尼水庫(kù)。 水庫(kù)年度泥沙淤積的不確定性和影響水庫(kù)泥沙淤積 的不確定性因素， 采取單獨(dú)和組合的方法審查水庫(kù)泥沙淤積進(jìn)程中的每個(gè)不確定 因素。結(jié)果表明，年流量和輸沙量是確定水庫(kù)年度泥沙淤積和泥沙積累過(guò)程變化 最重要的因素。 在肯尼水庫(kù)的情況下， 變差系數(shù)的不確定性可以大概表達(dá)為年度 水庫(kù)泥沙淤積的 65%和累積的水庫(kù)泥沙淤積量的 39%。簡(jiǎn)介水庫(kù)泥沙淤積因幾個(gè)因素的不同而變化， 例如產(chǎn)沙、 輸沙率、 沉積物類型、 泥沙淤積的模式、 水庫(kù)調(diào)度、 水庫(kù)的幾何形狀，河川徑流變化的模式等。泥沙

39、作為懸移質(zhì)和河床質(zhì)通過(guò)小溪和河流進(jìn)入水庫(kù)。 由于水流臨近水庫(kù)， 流速下降， 攜沙能力下降， 泥沙下沉并淤積在水庫(kù)中。 此外， 淤積的泥沙可能會(huì)因泥沙自身 重量和它上面的水的質(zhì)量的壓力而變得堅(jiān)固。 預(yù)測(cè)進(jìn)入水庫(kù)的泥沙通過(guò)時(shí)間積累 所造成的淤積問(wèn)題，將會(huì)是大壩施工以來(lái)一直存在在水利水電工程中的重要問(wèn) 題。盡管在理解關(guān)于水庫(kù)中所涉及的幾個(gè)因素的重要性方面取得了很大的進(jìn)展， 然而，預(yù)測(cè)水庫(kù)泥沙的淤積仍然是一個(gè)復(fù)雜的問(wèn)題。 以調(diào)查和實(shí)地觀察為基礎(chǔ)的 實(shí)證模型得到發(fā)展和應(yīng)用于估計(jì)每年水庫(kù)泥沙淤積負(fù)載(RSL)、 累積的水庫(kù)泥沙淤積負(fù)載 (ARSL )，并且計(jì)算運(yùn)行一定年限之后的水庫(kù)泥沙淤積的庫(kù)容 (ARS

40、V) 。同樣，預(yù)測(cè)水庫(kù)泥沙淤積的幾個(gè)基于運(yùn)動(dòng)方程和水沙連續(xù)性的數(shù)學(xué)模 型已經(jīng)發(fā)展。然而，在實(shí)際工程實(shí)踐中仍然廣泛使用實(shí)證方法。在估計(jì)水庫(kù)泥沙流入、 水庫(kù)泥沙沉淀和水庫(kù)泥沙淤積時(shí)， 只有通過(guò)實(shí)證或分 析的方法，一些不確定因素才能出現(xiàn)。影響水庫(kù)泥沙淤積的主要因素是(1)流 速 ；(2) 入庫(kù)泥沙量 ；(3) 泥沙顆粒大小 ；(4) 淤積的泥沙重量 ； (5) 水庫(kù)的 大小和運(yùn)行方式。根據(jù)眼前的一些特定情況， 一些因素可能會(huì)比其他因素更重要。 所有這些因素都有某種程度的不確定性， 因此，水庫(kù)泥沙淤積也將有很大程度的 不確定性。此外，一些模型 (或過(guò)程) 適用于一些上述某些因素的估計(jì)，但事 實(shí)上，另一

41、些用于估計(jì)淤積的模型， 在一座水庫(kù)中， 泥沙量可能是不能肯定地回 答的問(wèn)題。例如， Fan(1988 年) 關(guān)于對(duì) 34條溪流、 18 個(gè)流域和 20 個(gè)水庫(kù) 的泥沙淤積模型有關(guān)資料的分析， 指出不同的模型可能會(huì)有明顯不同的結(jié)果， 即 使使用相同的輸入數(shù)據(jù)。 這種附加因素， 稱為模型的不確定性， 可能是整體不確 定性中相當(dāng)大的組成部分。 在一些情況下， 設(shè)計(jì)者和水庫(kù)管理者可能會(huì)有興趣量 化一些影響水庫(kù)泥沙淤積因素的不確定性， 把它轉(zhuǎn)化為每年的泥沙淤積和泥沙淤 積通過(guò)時(shí)間變化的不確定性。 在一些論文中， 我們通過(guò)基于一組預(yù)定義的模型參 數(shù)來(lái)量化對(duì)水庫(kù)泥沙淤積的不確定性的影響來(lái)處理問(wèn)題。 在這些研

42、究中， 是不考 慮模型不確定性因素的影響。不確定性分析的幾種方法已被發(fā)展和運(yùn)用于水資源工程中。第一階分析( FOA) 和蒙特卡羅模擬 (MCS) 是最廣泛使用的方法。 FOA 基于線性關(guān)系， 涉及一個(gè)從屬的隨機(jī)變量和一套獨(dú)立的隨機(jī)變量的函數(shù)關(guān)系，由泰勒級(jí)數(shù)展開(kāi)。 這種方法涉及的不確定性已應(yīng)用在幾個(gè)水資源和環(huán)境工程的問(wèn)題中。 例子包括風(fēng) 暴下水道設(shè)計(jì)，地面水流估計(jì)，溶解氧的預(yù)測(cè)，潛流和污染物的運(yùn)輸估計(jì)等。在 MCS 中，隨機(jī)輸入通常從其概率分布分析，然后進(jìn)入實(shí)證或分析基于隨機(jī)產(chǎn)出 的潛在物理過(guò)程的模型。 然后，生成的產(chǎn)出分析統(tǒng)計(jì)是用以量化輸出的不確定性。 有很多通過(guò) MCS 所做的不確定性分析， 運(yùn)用于水資源和環(huán)境工程的例子。 這些 例子包括穩(wěn)態(tài)地面水流估計(jì)和水質(zhì)量建模。斯卡維亞 (1981 年 ) 作了一個(gè) MCS 和 FOA 的比較，用來(lái)確定浮游植物、 浮游動(dòng)物、 和氮形式等水體富營(yíng)養(yǎng)化模 型產(chǎn)生的不確定性。他們表示 MCS 和 FOA 都非常接近估計(jì)的平均值和方差模 型的估計(jì)數(shù)。然而， MCS 在提供更好地分發(fā)信息的輸出頻率方面更具有優(yōu)勢(shì)。拉丁立方體抽樣 (LHS) 是替代