Estimation of Sediment Trap Efficiency in Reservoirs-An Experimental Study -

Reservoir sedimentation has been a severe problem for most of the countries around the world. Reservoir sedimentation leads to the reduction of the original capacity which affects irrigation, hydropower, flood control, drinking water supply and recreational activities. As limited number of studies have been reported in this field and also whatever the available studies cover only few parameters which govern the trap efficiency of reservoirs. As a result, available formulations on trap efficiency are not very well defined. Brune [1953] curve is the most widely used method for estimating trap efficiency in reservoirs at present, but one of the limitations of this method is the use of only two parameters such as, reservoir capacity and inflow to estimate the trap efficiency. In order to investigate the factors affecting the trap efficiency, a smallscaled laboratory model was set-up. Several experiments were carried out by varying inflow rate, inflow sediment concentration, reservoir capacity and spillway length. The experimental data were analysed using dimensional analysis to develop an improved relationship to quantify reservoir sedimentation.


Introduction
Reservoir sedimentation is a severe problem around the world, as it reduces the original capacity of the reservoir significantly which affects the irrigation, hydropower and drinking water supply, flood control and recreational activities.Rantambe reservoir in Sri Lanka is one of the reservoirs severely affected by sedimentation.Due to lack of reservoir management practices such as periodical sediment flushing, reservoir sediment routing and catchment management to reduce the soil erosion, the sedimentation of reservoirs is inevitable and it has gradually becoming a greater threat for many countries around the world.Though reservoir sedimentation is a major threat many countries at present, only a limited number of studies have been reported in the field of reservoir sedimentation.Brune [4] and Churchill [5] have proposed empirical methods to estimate trap efficiency of reservoirs, but those methods are also found to be incomplete as they consider only few parameters which affect the reservoir sedimentation.This paper presents an experimental study carried out in order to formulate an improved methodology to quantify reservoir sedimentation.where, TE = trap efficiency V = reservoir capacity I = annual average inflow Brune [4] had considered only two parameters in his formulation namely, capacity and average annual inflow where many other factors affect the reservoir sedimentation.In addition, Brune [4] had used only normally ponded reservoirs for deriving this empirical relationship.However, his method found to be not accurate enough for reservoirs where a highly variable inflow can be observed, as trapped sediment weight is very much influenced by sediment inflow rate.Siyam [9] in his studies on reservoir sedimentation has concluded that Brune's curve [4] is a special case of a more general trap efficiency function given by the following equation; where, β is a sedimentation parameter that reflects the reduction in the reservoir storage capacity due to sedimentation.Further, Siyam [9] has also proposed some typical values for β which can define would welldefine the upper, median and lower Brune's curves [4], respectively as shown in Figure 1.Table 1 illustrates the relevant β value for each curve in the Figure 1.

Methodology 3.1 Experimental Set-up
The experimental set-up consisted of an overhead tank which has dimensions of 1.2m × 1.2m × 1m, to feed sediment laden water into the reservoir, a sediment agitator to keep sediments in suspension mode inside the overhead tank and a tank 0.5 m high, 1 m wide and 2 m long, to represent reservoir.The sediment agitator was consisted of an electric motor, vertical shaft with horizontal wings and a stand to fix the motor.Figure 4 shows a schematic diagram of the experimental set-up.

Figure 4: Schematic Diagram of Experimental Set-up
The rectangular tank was modified by placing four timber plates in the four comers in order to avoid water stagnation in those four corners.The reservoir bed was made linearly sloping towards the dam using compacted soil to represent the model much closer to a real reservoir.The dam of reservoir which was made by timber, has prepared in such a way where it can be moved back and forth to change the reservoir capacity (V).Actual photograph of the experimental set-up is presented in Figure 5.The trapped sediment weight (W s ) was plotted against the modelled parameters as shown in Figure 7, Figure 8, Figure 9 and Figure 10.The experiments were conducted for two different spillway lengths; 200 mm and 300 mm and the results were shown in Figure 7 where it shows a slight increment of trapped sediment weight (W s ) with spillway length (S) increases.

Figure 8: Variation of V versus W s
The trapped sediment weight (W s ) is plotted against the reservoir capacity (V) as in Figure 8 where the figure indicates the increment of W s with V.   Though the experimental data is very much scattered, it can be observed that there is an increasing trend relatively same profile as Brune curve [4].It could also be noted that there is different TE for the same V/I ratio indicating there are can be other parameters affecting TE.
Figure 12 shows the comparison of the experimental results with the Churchill curve [5], it shows a fare agreement with the curve though the results are somewhat scattered within a narrow region.But, it also can be observed that more than 50% of the results below than the curve.

Figure 12: Comparison of the experimental results with Churchill [5] curve
It is important to highlight that some of the researches have also discussed about the overestimation of TE in both the Brune [4] and Churchill [5] methods (Bashar et al [1] and Lewis et al [10]).The experimental results were also compared with Harbor et al [8], but the results were not agreed with Harbor et al [8] results.As Brune [4], Churchill [5] and Harbor et al [8] have not given a reasonable relationship for the experimental data obtained in this study a fresh data analysis has been adopted using Dimensional Analysis.
It is important to highlight that some of the researches have also discussed about the overestimation of TE in both the Brune [4] and Churchill [5] methods (Bashar et al [1] and Lewis et al [10]).The experimental results were also compared with Harbor et al [8], but the results were not agreed with Harbor et al [8] results.As Brune [4], Churchill [5] and Harbor et al [8] have not given a reasonable relationship for the experimental data obtained in this study a fresh data analysis has been adopted using Dimensional Analysis.

Dimensional analysis
The As in this study experimental time (t) and sediment density (ρ s ) were not varied.Therefore, the relative nondimensional terms are constants so they do not affect the deposited sediment amount for the present study.
The terms , are removed from the analysis.

Improved Correlations among Model Parameters
The non-dimensional parameter representing the trapped sediment weight (W s ) was plotted against the other non-dimensional parameters representing other model parameters which were obtained from the dimensional analysis.It has already been noted in Figure 10 that the inflow sediment concentration (C) has a dominant effect on trapped sediment weight (W s ) over the other model parameters.Figure 13 shows the relationship between the non-dimensional parameter representing inflow sediment concentration (C) and the trapped sediment weight (W s ). Figure 13 also presents a fairly good relationship between inflow sediment concentration (C) and the trapped sediment weight (W s ).But as there are more model parameter which examined in this study, the relationship was further developed adding the other model parameters.This shows the sediment inflow rate is dominant (I s ) parameter which affects the reservoir sedimentation.Combining all the model parameters, a new relationship was found as illustrated in Figure 15 where the amount of trapped sediments in the reservoir can be estimated using all the parameters considered in this study.This new formulae incorporates the reservoir capacity (V), sediment inflow rate (I s ), and the spillway length (S) which is directly related to reservoir outflow.This relationship indicates the trapped sediment weight (W s ) will reach a maximum value with increasing the term (I s .V/S 1/3 ).

Conclusions and Recommendations
A series of experiments have been conducted in order to investigate the sedimentation of reservoirs.The experimental data were analyzed to derive an improved formulation for the estimation of sediment trapped in reservoirs.The correlation as given in Equation 12, presents relationship between the non -dimensional terms representing trapped sediment weight (W s ) and the model parameters such as sediment inflow rate (I s ), reservoir capacity (V) and spillway length (S).
The newly derived equation as in Equation 12 can be applicable for predicting the quantity of sediment silted in a reservoir only during continuous spilling conditions.In addition, only one sediment gradation which has a mean diameter of about 0.02 mm was used in the experiments.However, the relationship developed during this study could be further improved by conducting more experimental runs by varying few other parameters which are not considered in the present study.
As the present study concentrates only on the continuous spilling condition of reservoirs, more experiments need to be conducted for non-spilling condition also.Further studies would need to account the operating rules where it is highly influence on the sediment outflow from the reservoir.Further studies can be carried out by varying the reservoir shape and orientation which are also important factors affecting the amount of sediment trapped in the reservoir.

FigureFigure 7 :
Figure 6: Particle Size Distribution of the Sediments

Figure 9 :
Figure 9: Variation of Q versus W s

Figure 10 :
Figure 10: Variation of C versus W s

Figure 13 :
Figure 13: Variation of Non-dimensional Terms Representing W s and C

Figure 14 :
Figure 14: Variation of Non-dimensional Terms Representing W s and CQ

Figure 15 :
Figure 15: Variation of Trapped Sediment Weight (W s ) with the Model Parameters