学位论文水库泥沙淤积的不确定因素分析外文翻译.docx

1、学位论文水库泥沙淤积的不确定因素分析 外文翻译外文翻译 Uncertainty Analysis Of Reservoir SedimentationAbstract: 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

2、estimating reservoir sedimentation 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 a

3、re used to quantify the 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

4、been applied to the Kenny 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 r

5、esults show that annual 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 6

6、5% for annual reservoir 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,

7、 and streamflow variability. Sediment is transported as suspended and bed loads by streams and rivers coming into a reservoir. Due to flow deceleration when a river approaches a reservoir, the sediment transport capacity decreases,and some of the incoming sediment is trapped and deposited in the res

8、ervoir. In addition, the 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 hydra

9、ulic engineering. Despite 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 es

10、timate annual reservoir sedimentation 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 devel

11、oped based on the equations 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

12、, a number of uncertainties 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 cas

13、e at hand, some factors may 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 fac

14、t, which model is to be used 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

15、may give significantly different results even when using the same set of input data. Such an additional factor, known as model uncertainty, may be 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 uncertai

16、nty of some of the factors affecting reservoir sedimentation translate into the uncertainty of annual sediment deposition and accumulated sediment deposition through time.In this paper, we address the issue quantifying the effect of parameter uncertainty on reservoir sedimentation based on a set of

17、predefined models as will be 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

18、(MCS). FOA is based on linearizing 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

19、 include storm sewer design; 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 underlyin

20、g physical process involved in 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-sta

21、te ground-water-flow estimation 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 esti

22、mating the mean and variance 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 s

23、ystems.The basic idea behind LHS 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 varia

24、nce than those obtained by the 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 th

25、e probability distribution of 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 p

26、rocedure, annual streamflows are 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, a

27、mong other factors,on the quantity 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 hydromet

28、ric gauging stations, and a sediment 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

29、of suspended sediment and bed 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

30、 the relation between annual sediment 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 da

31、ily sediment loads and divide 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

32、the record. An alternative method 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 sedimen

33、t loads. Colby (1956) stated that in actual practice daily sediment rating curves could be assumed to be equivalent to instantaneous sediment rating curves.Daily rating curves of suspended sediment and bed load may be represented as (1) (2)where QSD = daily suspended sediment load (tons/day); QBD= daily bed load (tons/day); QWD = daily average streamflow d