- Email: [email protected]
Re-look to conventional techniques for trapping efficiency estimation of a reservoir
International Journal of Sediment Research 23 (2008) 76-84
Re-look to conventional techniques for trapping efficiency estimation of a reservoir V. JOTHIPRAKASH 1 and Vaibhav GARG2
Abstract All reservoirs are subjected to sediment inflow and deposition up to a certain extent leading to reduction in their capacity. Thus, the important practical problem related to the life of reservoir is the estimation of sedimentation quantity in the reservoirs. Large number of methods and models are available for estimation of reservoir sedimentation process. However, each model differs greatly in terms of their complexity, inputs and other requirements. In the simplest way, the fraction of sediment deposit in the reservoir can be determined through the knowledge of its trap efficiency. Trap efficiency (Te) is the proportion of the incoming sediment that is deposited or trapped in a reservoir. Most of the Te estimation methods define a relationship of the Te of the reservoir to their capacity and annual inflow, generally through curves. In this study, the empirical relationships given by Brune and Brown were used and compared for estimating the trap efficiency of Gobindsagar Reservoir (Bhakra Dam) on Satluj River in Bilaspur district of Himachal Pradesh, in the Himalayan region of India. A new set of regression equations has been developed for Brune’s method and compared with Brown and other available Brune’s equations. It has been found that Brune’s equations developed in the present study estimated better than the other Brune’s equations reported in literature. Later, in the present study it was found that Brown’s approach was over estimating the Te. Hence it was again modified for Gobindsagar reservoir. It was also identified that sediments coming to this particular reservoir were mainly of coarse nature. Key Words: Reservoir sedimentation, Trap efficiency, Brown’s method, Brune’s method
1 Introduction Since the dawn of history, mankind has been trying to harness the available surface water resources by building dams and creating reservoirs. The estimation of the total storage capacity of reservoirs in the world has been reported by various sources. One such is 4,000 to 6,000 x 109 m3, and another is 5 per cent of the total runoff in the world (38,830 x 109 m3), i.e. 2,000 x 109 m3 (Yang, 2003). While according to Siyam et al. (2005), the potential quantity of water that can be controlled in the future varies between 9,000 and 14,000 km3 annually. Inspite of multi purpose benefits, reservoirs are also having some drawbacks. Prominent among these are the interruption of the natural flow regimes and natural eco-systems, land inundation and required resettlement, water quality degradation, downstream river bed degradation and reservoir sedimentation. Reservoirs are often threatened by loss of capacity due to sedimentation. Reservoir sedimentation is the process of sediment deposition into a lake formed after a dam construction. A dam causes reduction in flow velocity and consequently the turbulence, which causes the settling process of the materials carried by the rivers. Globally, the overall annual loss rate of reservoir storage capacity due to sedimentation is 1
Assistant Professor and 2 Research scholar, Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai – 400 076, Maharashtra, India. E-mail: [email protected] and [email protected] Note: The original manuscript of this paper was received in Oct. 2006. The revised version was received in July 2007. Discussion open until March 2009. - 76 -
International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
estimated as 1 to 2 per cent of the total storage capacity (Yang, 2003; Campos, 2001). Some reservoirs are filled very rapidly, while others are hardly affected by sedimentation. In India, many reservoirs have been subjected to reduction in their storage capacities due to sedimentation. Analysis of sedimentation survey details in respect of 43 major, medium and minor reservoirs in the country indicated that the sedimentation rate varies between 0.34 – 27.85 ha m/100 km2/ year for major reservoirs, 0.15 – 10.65 ha m/ 100 km2/ year for medium reservoirs and 1.0 – 2.3 ha m/ 100 km2/ year for minor reservoirs (Shangle, 1991). More than 3,000 major and medium river valley projects have been constructed in India for serving various conservation purposes (Durbude and Purandara, 2005). The range of problems caused by reservoir sedimentation is varied and wide. Apart from already mentioned ones like loss of capacity, increased flood risks, interruption in hydropower generation and downstream river bed degradation; there are some other problems such as degradation of water quality, increased complexity in reservoir operation and maintenance and consequent increase in their associated cost. Methods to predict reservoir sedimentation have been the subject of several empirical studies since the 1950’s. Watershed, sediment and river characteristics are among the main natural contributing factors to reservoir sedimentation. The dominant factors that influence the rate of silting in a reservoir are: (a) Capacity to Inflow Ratio (C/I), (b) sediment content in the water flowing in, (c) texture and size of the sediment, (d) trap efficiency (Te) of the reservoir, and (e) the method of reservoir operation (Arora and Goel, 1994). In the present study, trap efficiency of Gobindsagar Reservoir (Bhakra Dam) on Satluj River in Bilaspur district, Himachal Pradesh, in the foothill of Himalaya, India was estimated using Brown (1944) and Brune (1953) approach. A new set of regression equations were derived to estimate the trap efficiency using Brune’s approach. Finally results were compared with the reported (Gill, 1979) regression equations for estimating trap efficiency using Brune’s method. 2 Study area The Bhakra dam is one of the oldest dams in India, and was commissioned in 1958. Bhakra Dam is constructed on Satluj River, Bilaspur district, Himachal Pradesh, in the foothills of Himalayan region, India leading to the creation of Gobindsagar reservoir. The location of the Gobindsagar reservoir is shown in Fig. 1. Gobindsagar reservoir has a designed total storage capacity of 9,867.84 x 106 m3. The reservoir has an enormous water spread area extending over 168.35 km2 at full reservoir level. The river Satluj originating from Mansarover Lake along with its tributaries has a catchment area of 56,876 km2 while negotiating through vivid terrains and transports lot of sediment into the reservoir affecting its life. This region is also very prone to landslides and slips which may be one of the major sources of sediment in this river. The natural factors that also attribute to high levels of sediment transport from the study region are steep topographic gradient, poor structural characteristics of soils; clay rich rocks such as Spiti Shale and Schist; and the widespread existence of limestone deposits (Sharma et al., 1991).
Fig. 1 The location of Gobindsagar Reservoir on Satluj River (Jain et al., 2002) International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
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Bhakra Beas Management Board (BBMB), the controlling agency of Gobindsagar reservoir, conducts reservoir capacity surveys (using Range Survey method) regularly to measure actual silt deposited in the reservoir. The Bhakra Reservoir is fed by the flow consisting of contribution from rainfall and snowmelt. Singh and Kumar (1997) studied precipitation distribution for several Himalayan basins and found that the maximum contribution to annual rainfall (42–60%) is received during the monsoon season, whereas the minimum (5–10%) is received in the post-monsoon season. Consequently, the reservoir attains its maximum water level just after the monsoon season. The water level of the reservoir gradually reduces due to releases for various uses and reaches lower levels before the onset of the next monsoon; this is the period during which the sediments are trapped in the reservoir. 3 Trap efficiency (Te) and its estimation Heinemann (1981) considered trapping efficiency to be the most informative descriptor of a reservoir. This value is the proportion of the incoming sediment that is deposited or trapped in a pond, reservoir, or lake; often expressed in percentage as given in equation 1. V − VO × 100 (1) Te = I VI where, VI is the inflowing sediment load and VO is the outflow sediment load. The Te is dependent on several parameters, like sediment size, distribution; the time and rate of water inflow to the reservoir; the reservoir size and shape; the location of the outlet structure and water discharge schedules (Yang, 1996; Morris and Fan, 1998; Verstraeten and Poesen, 2000; Campos, 2001; Yang, 2003). Many empirical studies (Brown, 1944; Churchill, 1948; Brune, 1953) showing the relation between reservoir storage capacity, water inflow, and trapping efficiency have been reported in the literature. Most of the methods define a relationship of Te with the capacity and average annual inflow parameters of the reservoir, generally through curves. The first trap efficiency (Te) estimation method was the pioneer work by Brown in 1944. USACE (1989) expressed this method as Capacity-Watershed method because Brown’s curve relating the ratio of the reservoir capacity (C, in acre-feet) and the catchment/watershed area (A, in square miles) to trap efficiency (Te, in %). This curve, (shown in Fig. 2) can be represented by a general equation for Te (originally called E by the author), which is given in eq. 2 (Gill, 1979; Campos, 2001; USACE, 1989)
Fig. 2 Brown’s (1944) trap efficiency curve (USACE, 1989)
Te = 1 −
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1 C⎞ ⎛ ⎜1 + κ ⎟ A⎠ ⎝
(2)
International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
where C, is capacity of the reservoir in acre ft.; A, is area of the catchment above the reservoir in mi2 and κ is a coefficient which varies from 0.046 to 1.0. According to USACE (1989), κ increase with (i) smaller and varied retention time (calculated using capacity-inflow ratio), (ii) as the average grain size increases and (iii) for reservoir operations that prevents release of sediment through sluicing or movement of sediment towards the outlets by pool elevation regulation. A value of κ = 0.1 was recommended for average conditions, and values of κ = 1.0, 0.1 and 0.046 may be used for coarse, medium and fine sediments, respectively (Gill, 1979). The major advantage of Brown’s method is only two parameters required i.e., catchment area and reservoir capacity. After Brown’s method, the next method for Te prediction was Churchill’s (1948) method. Churchill established a relation between the sedimentation index (SI) and the sediment load flushed by the reservoir (100 – Te), in percentage also called as release efficiency. Churchill defined two curves, one is for fine silt discharged from an upstream reservoir and the other is for local silt, i.e. for sediment originated in the catchment. Churchill’s method is limited to estimate the release efficiency in settling basins, small reservoirs, flood retarding structures, semidry reservoirs, or reservoirs that are continuously sluiced (Morris and Fan, 1998; Murthy, 1980). Following Churchill, the next method to estimate Te was Brune (1953). This method is probably the most widely used method for estimating the trap efficiency of reservoirs. Brune’s curves were drawn based on data from 44 normal ponded reservoirs in the United States. Brune plotted Te against the reservoir C/I ratio. The graph plotted by Brune has three curves consisting of one median and two envelop curves (Fig. 3).
Fig. 3 Brune’s curve for estimating sediment trapping efficiency (Brune, 1953)
The variations, as shown by the envelope curves, are due to the same reasons that influence the of Brown’s method. However, according to USACE (1989), Brune’s curve method is considered to be more accurate than Brown’s curve method. Later, Dendy (1974), Gill (1979) and Heinemann (1981) modified Brune’s method and developed algebraic equations which provide a very close fit to the three curves proposed by Brune. However, the method by Dendy (1974) and Heinnemann (1981) have restrictions in their use as they were developed with data of small reservoirs. Gill (1979) proposed the following equation (Eq. 3) as a very close fit to Brune’s highly flocculated and coarse grained sediments envelope curve.
κ coefficient
Te =
(C / I )2
0.994701(C / I )2 + 0.006297(C / I ) + 0.3 × 10 −5
International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
(3) - 79 -
4 Results and discussion In the present study, it was aimed to estimate the trap efficiency for Gobindsagar reservoir. The controlling agency BBMB, had conducted capacity surveys for Gobindsagar reservoir annually from 1963 to 1977 to measure the actual silt deposited. Thereafter these surveys were further being carried out on alternate years. According to the surveys carried out so far (from 1962-2003), the average annual rate of siltation has been worked out as 34.552 x 106 m3 against a designed figure of 33.61 x 106 m3 (BBMB, 2003). Knowing the sediment inflow and sediment outflow, BBMB also calculated the average trap efficiency as 99.4% using Eq. 1, and reported that the overall capacity loss of reservoir is 15.67%. Since, the sediment inflow and outflow are generally not measured at all the hydrological projects in India, empirical approaches like Brown and Brune may be the best suitable methods to estimate sediment retention. Observed values of Te at Gobindsagar reservoir were plotted on Brune’s curves (Fig. 4) and it was found to follow the trend of primarily highly flocculated and coarse grained sediments envelope curve. Therefore, it may be assumed that mainly the sediments coming in this reservoir are coarser in nature. However finer sediments were also reported but lesser in quantity.
Fig. 4 Location of observed Te on Brune’s curve
In this study, as an initial step, the best fit regression equations to the Brune’s median and coarse-grained sediment envelope curves were developed and reported in equations 4 and 5 respectively. For primarily highly flocculated and coarse grained sediments: −0.78
⎛C ⎞ 8000 − 36 × ⎜ ⎟ ⎝I ⎠ Te = − 0.78 ⎛C ⎞ 78.85 + ⎜ ⎟ ⎝I ⎠
(4)
Median curve (for medium sediments) ⎛C ⎞ ⎜ ⎟ ⎝I ⎠ Te = (5) ⎛ ⎞ C C ⎜ 0.00013 + 0.01 × + 0.0000166 × ⎟ ⎜ I I ⎟⎠ ⎝ Since, the incoming sediments this large reservoir are considered coarser in nature (as per Fig. 4), Brune’s coarse sediment equations, Gill equation (Eq. 3) and above developed Eq. 4 of the present study were - 80 -
International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
more useful. To find the best fit equation the Gill’s equation and the equation obtained from the present study have been plotted over the original coarse sediment envelope curve of Brune as shown in Fig. 5. It is clear from the Fig. 5, that the regression equation developed in the present study follows the original Brune’s curve better than the Gill’s curve. Thus, it may be concluded that the present study regression equation suits better for coarse sediments curve equation than Gill’s method.
Fig. 5 Comparative plot of present study, Gill’s and Brune’s original coarse sediment envelope curves
However, from Fig. 4 it can be seen that the sediments coming into the reservoir are slightly above the coarse sediments envelope curve. Hence the parameters in Eq. 4 modified accordingly and are reported in Eq. 6. It should be noted that the equation (Eq. 6) is only applicable for this particular reservoir. Hence, for further analysis Gill’s method (Eq. 3), regression equation (Eq.6) from present study and Brown’s method (Eq. 2) were used in place of Dendy (1974) and Heinemann (1981) methods due to their limitations of being applicable to only small reservoirs. The trap efficiency estimated using the above selected coarse grained sediment equations have been reported in Table 1. −0.8 ⎛C ⎞ 8005 − 35 × ⎜ ⎟ ⎝I ⎠ Te = (6) − 0 .1 ⎛C ⎞ 78.85 + ⎜ ⎟ ⎝I ⎠ The capacity and average annual inflow into the reservoir from 1963-2003 has also been presented in Table 1. From the table it can be seen that capacity of reservoir is reducing as inflow is showing an increasing trend. The Te estimated using mass balance equation (given as Eq. 1) is shown in Table 1. It is found that during first two years the observed Te is very less but after the third observation Te is reducing from higher values to lower with a slight variation. All the estimated methods have also shown the decreasing trend of Te. The initial two data sets have not been captured by any of the methods. Earlier the Te by Brown method has been estimated using κ = 1.0 for large reservoirs, as suggested by Gill (1979). But it was found that the value κ = 1.0 is overestimating Te. Hence, the constant κ was modified to κ = 0.58 (this value is equal to the average measured C/I ratio) and the Te was estimated by using Brown’s method (Table 1). This estimated Te closely followed the observed Te. Hence, it may be concluded that for the present study area (Gobindsagar reservoir), κ = 0.58 is an appropriate value in Brown’s method. The comparative plot of all the above methods and observed trap efficiency is also shown in Fig.6. The trend of the observed Te is showing a decreasing trap efficiency which is modelled well by all the methods. Brown’s approach worked well with κ = 0.58 for the present study area. The Gill’s method, which is based on Brune’s work, estimated better till 1977, but immediately after 1977, this method started underestimating the Te. This may be because of the range of capacity inflow ratio (C/I) changes International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
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from larger to medium reservoir condition during this period. This disadvantage is overcome by the equation deduced in the present study. The regression equation developed in the present study for Brune’s primarily highly flocculated and coarse grained sediments envelope curve performed better than any other method, as the estimated trend is quite close to the observed trend (as shown in Fig. 6). Hence, based on the present study, it may be concluded that the Brown’s method with κ = 0.58 and Brune’s regression equation developed in this study may be used for estimating trap efficiency of a Gobindsagar reservoir. Table 1 Comparison of observed and estimated trap efficiencies Year
Capacity (measured) (C, in x 106 m3)
C/I Ratio (observed)
Observed Te (%) (Eq. 1)
1963 1964 1967 1968 1969 1971 1972 1973 1974 1975 1976 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003
9,867.84 9,724.54 9,556.01 9,523.55 9,507.11 9,447.57 9,417.72 9,395.39 9,341.83 9,320.95 9,285.25 9,259.83 9,160.32 9,091.14 9,034.62 9,007.28 8,932.94 8,855.71 8,736.25 8,681.57 8,585.29 8,533.49 8,477.62 8,385.58 8,321.76
0.651 0.641 0.630 0.628 0.627 0.623 0.621 0.620 0.616 0.615 0.613 0.613 0.588 0.579 0.566 0.560 0.550 0.536 0.522 0.513 0.504 0.497 0.491 0.487 0.485
98.40 98.40 99.80 99.70 99.50 99.00 99.60 99.00 99.60 99.40 99.50 99.30 99.80 99.60 99.50 99.10 99.40 99.50 99.60 99.40 99.50 99.30 99.20 99.50 99.40
Coarse-grained sediment Brown (%) Gill (Eq. 2) (%) (Eq. 3) ( κ = 0.58) ( κ = 1.0) 99.73 99.53 99.56 99.72 99.52 99.55 99.72 99.52 99.53 99.72 99.51 99.53 99.72 99.51 99.53 99.71 99.51 99.52 99.71 99.51 99.52 99.71 99.51 99.52 99.71 99.50 99.51 99.71 99.50 99.51 99.71 99.50 99.50 99.71 99.50 99.50 99.71 99.49 99.46 99.70 99.49 99.44 99.70 99.49 99.42 99.70 99.49 99.41 99.70 99.48 99.39 99.70 99.48 99.36 99.69 99.47 99.33 99.69 99.47 99.31 99.69 99.46 99.28 99.68 99.46 99.27 99.68 99.45 99.25 99.68 99.45 99.24 99.68 99.44 99.24
Present study (%) (Eq. 6) 99.58 99.57 99.56 99.56 99.55 99.55 99.55 99.55 99.54 99.54 99.54 99.54 99.51 99.50 99.49 99.48 99.47 99.45 99.43 99.42 99.40 99.39 99.38 99.38 99.38
Fig. 6 Comparative plot of Te estimation with different methods - 82 -
International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
5 Conclusions The sediment trap efficiency (Te) of Gobindsagar Reservoir (Bhakra Dam) on Satluj River in Bilaspur district, Himachal Pradesh, in foothill of the Himalaya, India has been estimated using Brune’s and Brown’s methods. For estimating Te using Brune’s method, new regression equation was developed for Brune’s primarily highly flocculated and coarse grained sediments envelope curve. The estimated Te was compared with the measured Te as well as Gill’s approach. It was found that the trend of results closely follow the Brune’s coarse sediment envelope curve which shows that the sediments in this particular reservoir are mainly coarse sediments in nature. Finally, the results were compared with solution of Brown’s method with κ = 1.0 and it was found that Brown’s method overestimated the value of Te. Hence, the constant κ was modified to κ = 0.58 (average of the observed C/I values). The modified Brown’s method ( κ = 0.58) and present study regression equation have been found to be best suited for estimating the trap efficiency in the present study area, Gobindsagar Reservoir. It was also found that, the major advantage of these empirical methods was to give fairly reasonable results from very limited data: storage volume, average annual inflow and catchment area. As a limitation, the methods are applicable only to long-term average conditions. In the country like India where the sediment inflow and outflow are not usually measured, these empirical approaches are the best suitable approach to estimate sediment retention. Acknowledgements The authors would like to thank Dr. Sanjay Kr. Jain and Mr. Ajanta Goswami for the help extended. We gratefully acknowledge the anonymous reviewers and editors for their reviews and suggestions. Reference Arora P. K. and Goel M. P. 1994, Estimating life of a reservoir. Proc. of Workshop on Reservoir Sedimentation, Mysore (Karnataka) May 17-19, pp. 4-11. BBMB (Bhakra Beas Management Board) 2003, Sedimentation Survey Report. Bhakra Dam Circle, Nangal, India Brown C. B. 1944, Discussion of sedimentation in reservoirs. Ed. J. Witzig. Proc. of the American Society of Civil Engineers, Vol. 69, pp. 1493-1500. Brune G. M. 1953, Trap efficiency of reservoirs. Trans. Am. Geophysical Union, Vol. 34, No. 3, pp. 407-418. Campos R. 2001, Three-dimensional reservoir sedimentation model. PhD Thesis, Department of Civil Engineering, University of Newcastle, Newcastle. Churchill M. A. 1948, Discussion of analysis and use of reservoir sedimentation data. Ed. L. C. Gottschalk, Proc. of Federal Interagency Sedimentation Conference, Denver, pp. 139-140. Dendy F. E. 1974, Sediment trap efficiency of small reservoirs. Trans. of ASAE, Vol. 17, No. 5, pp. 898-988. Durbude G. D. and Purandara B. K. 2005, Assessment of sedimentation in the Linganmakki Reservoir using remote sensing. Journal of the Indian Society of Remote Sensing (Photonirvachak), Vol. 33, No. 4, pp. 503-510. Gill M. A. 1979, Sedimentation and useful life of reservoirs. Journal of Hydrology, Vol. 44, pp. 89-95. Heinemann H. G. 1981, A new sediment trap efficiency curve for small reservoirs. Water Resources Bulletin, Vol. 17, No. 5, pp. 825-830. Jain S. K., Singh P., and Seth S. M. 2002, Assessment of sedimentation in Bhakra Reservoir in the western Himalayan Region using remotely sensed data. Hydrological Sciences Journal, Vol. 47, No.2, pp. 203-212. Lagwankar V. G., Gorde A. K., and Patil K. D. 1994, Trends in reservoir sedimentation in India and abroad. Proc. of Workshop on Reservoir Sedimentation, Mysore (Karnataka) May 17-19, pp. 127-134. Morris G. L. and Fan J. 1998, Reservoir Sedimentation Handbook, McGraw Hill, New York, USA. Murthy B. N. 1980, Life of reservoir. Technical Report No. 19, Central Board of Irrigation and Power (CBIP), New Delhi. Shangale A. K. 1991, Reservoir Sedimentation Status in India, Jalvigyan Sameeksha, INCOH, NIH Roorkee, VI (1&2). Sharma P. D., Goel A. K., and Minhas, R. S. 1991, Water and sediment yields into the Satluj River from the high Himalaya. Mountain Res. Devel. Vol. 11, No. 2, pp. 87–100. Singh P. and Kumar N. 1997, Effect of orography on precipitation in the western Himalayan Region. Journal of Hydrology, Vol. 199, pp.183–206. Siyam A. M., Mirghani M., El zein S., Golla S., and El-sayed S. M. 2005, Assessment of the current state of the Nile basin reservoir sedimentation problems. NBCN-RE (Nile Basin Capacity Building Network for River Engineering), River Morphology, Research Cluster, Group–I Report. USACE (U.S. Army Corps of Engineers) 1989, Engineering and design: Sedimentation investigations of rivers and reservoirs. Engineering Manual 1110-2-4000, Washington, D.C. International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
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Verstraeten G. and Posen J. 2000, Estimating trap efficiency of small reservoirs and ponds: Methods and implications for the assessment of sediment yield. Progress in Physical Geography, Vol. 24, pp. 219-251. Yang C. T. 1996, Sediment transport: Theory and Practice, McGraw-Hill, New York. Yang Xiaoqing. 2003, Manual on sediment management and measurement, World Meteorological Organization, Operational Hydrology Report No. 47, WMO-No. 948, Secretariat of the World Meteorological Organization–Geneva, Switzerland.
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International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
Re-look to conventional techniques for trapping efficiency estimation of a reservoir V. JOTHIPRAKASH 1 and Vaibhav GARG2
Abstract All reservoirs are subjected to sediment inflow and deposition up to a certain extent leading to reduction in their capacity. Thus, the important practical problem related to the life of reservoir is the estimation of sedimentation quantity in the reservoirs. Large number of methods and models are available for estimation of reservoir sedimentation process. However, each model differs greatly in terms of their complexity, inputs and other requirements. In the simplest way, the fraction of sediment deposit in the reservoir can be determined through the knowledge of its trap efficiency. Trap efficiency (Te) is the proportion of the incoming sediment that is deposited or trapped in a reservoir. Most of the Te estimation methods define a relationship of the Te of the reservoir to their capacity and annual inflow, generally through curves. In this study, the empirical relationships given by Brune and Brown were used and compared for estimating the trap efficiency of Gobindsagar Reservoir (Bhakra Dam) on Satluj River in Bilaspur district of Himachal Pradesh, in the Himalayan region of India. A new set of regression equations has been developed for Brune’s method and compared with Brown and other available Brune’s equations. It has been found that Brune’s equations developed in the present study estimated better than the other Brune’s equations reported in literature. Later, in the present study it was found that Brown’s approach was over estimating the Te. Hence it was again modified for Gobindsagar reservoir. It was also identified that sediments coming to this particular reservoir were mainly of coarse nature. Key Words: Reservoir sedimentation, Trap efficiency, Brown’s method, Brune’s method
1 Introduction Since the dawn of history, mankind has been trying to harness the available surface water resources by building dams and creating reservoirs. The estimation of the total storage capacity of reservoirs in the world has been reported by various sources. One such is 4,000 to 6,000 x 109 m3, and another is 5 per cent of the total runoff in the world (38,830 x 109 m3), i.e. 2,000 x 109 m3 (Yang, 2003). While according to Siyam et al. (2005), the potential quantity of water that can be controlled in the future varies between 9,000 and 14,000 km3 annually. Inspite of multi purpose benefits, reservoirs are also having some drawbacks. Prominent among these are the interruption of the natural flow regimes and natural eco-systems, land inundation and required resettlement, water quality degradation, downstream river bed degradation and reservoir sedimentation. Reservoirs are often threatened by loss of capacity due to sedimentation. Reservoir sedimentation is the process of sediment deposition into a lake formed after a dam construction. A dam causes reduction in flow velocity and consequently the turbulence, which causes the settling process of the materials carried by the rivers. Globally, the overall annual loss rate of reservoir storage capacity due to sedimentation is 1
Assistant Professor and 2 Research scholar, Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai – 400 076, Maharashtra, India. E-mail: [email protected] and [email protected] Note: The original manuscript of this paper was received in Oct. 2006. The revised version was received in July 2007. Discussion open until March 2009. - 76 -
International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
estimated as 1 to 2 per cent of the total storage capacity (Yang, 2003; Campos, 2001). Some reservoirs are filled very rapidly, while others are hardly affected by sedimentation. In India, many reservoirs have been subjected to reduction in their storage capacities due to sedimentation. Analysis of sedimentation survey details in respect of 43 major, medium and minor reservoirs in the country indicated that the sedimentation rate varies between 0.34 – 27.85 ha m/100 km2/ year for major reservoirs, 0.15 – 10.65 ha m/ 100 km2/ year for medium reservoirs and 1.0 – 2.3 ha m/ 100 km2/ year for minor reservoirs (Shangle, 1991). More than 3,000 major and medium river valley projects have been constructed in India for serving various conservation purposes (Durbude and Purandara, 2005). The range of problems caused by reservoir sedimentation is varied and wide. Apart from already mentioned ones like loss of capacity, increased flood risks, interruption in hydropower generation and downstream river bed degradation; there are some other problems such as degradation of water quality, increased complexity in reservoir operation and maintenance and consequent increase in their associated cost. Methods to predict reservoir sedimentation have been the subject of several empirical studies since the 1950’s. Watershed, sediment and river characteristics are among the main natural contributing factors to reservoir sedimentation. The dominant factors that influence the rate of silting in a reservoir are: (a) Capacity to Inflow Ratio (C/I), (b) sediment content in the water flowing in, (c) texture and size of the sediment, (d) trap efficiency (Te) of the reservoir, and (e) the method of reservoir operation (Arora and Goel, 1994). In the present study, trap efficiency of Gobindsagar Reservoir (Bhakra Dam) on Satluj River in Bilaspur district, Himachal Pradesh, in the foothill of Himalaya, India was estimated using Brown (1944) and Brune (1953) approach. A new set of regression equations were derived to estimate the trap efficiency using Brune’s approach. Finally results were compared with the reported (Gill, 1979) regression equations for estimating trap efficiency using Brune’s method. 2 Study area The Bhakra dam is one of the oldest dams in India, and was commissioned in 1958. Bhakra Dam is constructed on Satluj River, Bilaspur district, Himachal Pradesh, in the foothills of Himalayan region, India leading to the creation of Gobindsagar reservoir. The location of the Gobindsagar reservoir is shown in Fig. 1. Gobindsagar reservoir has a designed total storage capacity of 9,867.84 x 106 m3. The reservoir has an enormous water spread area extending over 168.35 km2 at full reservoir level. The river Satluj originating from Mansarover Lake along with its tributaries has a catchment area of 56,876 km2 while negotiating through vivid terrains and transports lot of sediment into the reservoir affecting its life. This region is also very prone to landslides and slips which may be one of the major sources of sediment in this river. The natural factors that also attribute to high levels of sediment transport from the study region are steep topographic gradient, poor structural characteristics of soils; clay rich rocks such as Spiti Shale and Schist; and the widespread existence of limestone deposits (Sharma et al., 1991).
Fig. 1 The location of Gobindsagar Reservoir on Satluj River (Jain et al., 2002) International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
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Bhakra Beas Management Board (BBMB), the controlling agency of Gobindsagar reservoir, conducts reservoir capacity surveys (using Range Survey method) regularly to measure actual silt deposited in the reservoir. The Bhakra Reservoir is fed by the flow consisting of contribution from rainfall and snowmelt. Singh and Kumar (1997) studied precipitation distribution for several Himalayan basins and found that the maximum contribution to annual rainfall (42–60%) is received during the monsoon season, whereas the minimum (5–10%) is received in the post-monsoon season. Consequently, the reservoir attains its maximum water level just after the monsoon season. The water level of the reservoir gradually reduces due to releases for various uses and reaches lower levels before the onset of the next monsoon; this is the period during which the sediments are trapped in the reservoir. 3 Trap efficiency (Te) and its estimation Heinemann (1981) considered trapping efficiency to be the most informative descriptor of a reservoir. This value is the proportion of the incoming sediment that is deposited or trapped in a pond, reservoir, or lake; often expressed in percentage as given in equation 1. V − VO × 100 (1) Te = I VI where, VI is the inflowing sediment load and VO is the outflow sediment load. The Te is dependent on several parameters, like sediment size, distribution; the time and rate of water inflow to the reservoir; the reservoir size and shape; the location of the outlet structure and water discharge schedules (Yang, 1996; Morris and Fan, 1998; Verstraeten and Poesen, 2000; Campos, 2001; Yang, 2003). Many empirical studies (Brown, 1944; Churchill, 1948; Brune, 1953) showing the relation between reservoir storage capacity, water inflow, and trapping efficiency have been reported in the literature. Most of the methods define a relationship of Te with the capacity and average annual inflow parameters of the reservoir, generally through curves. The first trap efficiency (Te) estimation method was the pioneer work by Brown in 1944. USACE (1989) expressed this method as Capacity-Watershed method because Brown’s curve relating the ratio of the reservoir capacity (C, in acre-feet) and the catchment/watershed area (A, in square miles) to trap efficiency (Te, in %). This curve, (shown in Fig. 2) can be represented by a general equation for Te (originally called E by the author), which is given in eq. 2 (Gill, 1979; Campos, 2001; USACE, 1989)
Fig. 2 Brown’s (1944) trap efficiency curve (USACE, 1989)
Te = 1 −
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1 C⎞ ⎛ ⎜1 + κ ⎟ A⎠ ⎝
(2)
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where C, is capacity of the reservoir in acre ft.; A, is area of the catchment above the reservoir in mi2 and κ is a coefficient which varies from 0.046 to 1.0. According to USACE (1989), κ increase with (i) smaller and varied retention time (calculated using capacity-inflow ratio), (ii) as the average grain size increases and (iii) for reservoir operations that prevents release of sediment through sluicing or movement of sediment towards the outlets by pool elevation regulation. A value of κ = 0.1 was recommended for average conditions, and values of κ = 1.0, 0.1 and 0.046 may be used for coarse, medium and fine sediments, respectively (Gill, 1979). The major advantage of Brown’s method is only two parameters required i.e., catchment area and reservoir capacity. After Brown’s method, the next method for Te prediction was Churchill’s (1948) method. Churchill established a relation between the sedimentation index (SI) and the sediment load flushed by the reservoir (100 – Te), in percentage also called as release efficiency. Churchill defined two curves, one is for fine silt discharged from an upstream reservoir and the other is for local silt, i.e. for sediment originated in the catchment. Churchill’s method is limited to estimate the release efficiency in settling basins, small reservoirs, flood retarding structures, semidry reservoirs, or reservoirs that are continuously sluiced (Morris and Fan, 1998; Murthy, 1980). Following Churchill, the next method to estimate Te was Brune (1953). This method is probably the most widely used method for estimating the trap efficiency of reservoirs. Brune’s curves were drawn based on data from 44 normal ponded reservoirs in the United States. Brune plotted Te against the reservoir C/I ratio. The graph plotted by Brune has three curves consisting of one median and two envelop curves (Fig. 3).
Fig. 3 Brune’s curve for estimating sediment trapping efficiency (Brune, 1953)
The variations, as shown by the envelope curves, are due to the same reasons that influence the of Brown’s method. However, according to USACE (1989), Brune’s curve method is considered to be more accurate than Brown’s curve method. Later, Dendy (1974), Gill (1979) and Heinemann (1981) modified Brune’s method and developed algebraic equations which provide a very close fit to the three curves proposed by Brune. However, the method by Dendy (1974) and Heinnemann (1981) have restrictions in their use as they were developed with data of small reservoirs. Gill (1979) proposed the following equation (Eq. 3) as a very close fit to Brune’s highly flocculated and coarse grained sediments envelope curve.
κ coefficient
Te =
(C / I )2
0.994701(C / I )2 + 0.006297(C / I ) + 0.3 × 10 −5
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(3) - 79 -
4 Results and discussion In the present study, it was aimed to estimate the trap efficiency for Gobindsagar reservoir. The controlling agency BBMB, had conducted capacity surveys for Gobindsagar reservoir annually from 1963 to 1977 to measure the actual silt deposited. Thereafter these surveys were further being carried out on alternate years. According to the surveys carried out so far (from 1962-2003), the average annual rate of siltation has been worked out as 34.552 x 106 m3 against a designed figure of 33.61 x 106 m3 (BBMB, 2003). Knowing the sediment inflow and sediment outflow, BBMB also calculated the average trap efficiency as 99.4% using Eq. 1, and reported that the overall capacity loss of reservoir is 15.67%. Since, the sediment inflow and outflow are generally not measured at all the hydrological projects in India, empirical approaches like Brown and Brune may be the best suitable methods to estimate sediment retention. Observed values of Te at Gobindsagar reservoir were plotted on Brune’s curves (Fig. 4) and it was found to follow the trend of primarily highly flocculated and coarse grained sediments envelope curve. Therefore, it may be assumed that mainly the sediments coming in this reservoir are coarser in nature. However finer sediments were also reported but lesser in quantity.
Fig. 4 Location of observed Te on Brune’s curve
In this study, as an initial step, the best fit regression equations to the Brune’s median and coarse-grained sediment envelope curves were developed and reported in equations 4 and 5 respectively. For primarily highly flocculated and coarse grained sediments: −0.78
⎛C ⎞ 8000 − 36 × ⎜ ⎟ ⎝I ⎠ Te = − 0.78 ⎛C ⎞ 78.85 + ⎜ ⎟ ⎝I ⎠
(4)
Median curve (for medium sediments) ⎛C ⎞ ⎜ ⎟ ⎝I ⎠ Te = (5) ⎛ ⎞ C C ⎜ 0.00013 + 0.01 × + 0.0000166 × ⎟ ⎜ I I ⎟⎠ ⎝ Since, the incoming sediments this large reservoir are considered coarser in nature (as per Fig. 4), Brune’s coarse sediment equations, Gill equation (Eq. 3) and above developed Eq. 4 of the present study were - 80 -
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more useful. To find the best fit equation the Gill’s equation and the equation obtained from the present study have been plotted over the original coarse sediment envelope curve of Brune as shown in Fig. 5. It is clear from the Fig. 5, that the regression equation developed in the present study follows the original Brune’s curve better than the Gill’s curve. Thus, it may be concluded that the present study regression equation suits better for coarse sediments curve equation than Gill’s method.
Fig. 5 Comparative plot of present study, Gill’s and Brune’s original coarse sediment envelope curves
However, from Fig. 4 it can be seen that the sediments coming into the reservoir are slightly above the coarse sediments envelope curve. Hence the parameters in Eq. 4 modified accordingly and are reported in Eq. 6. It should be noted that the equation (Eq. 6) is only applicable for this particular reservoir. Hence, for further analysis Gill’s method (Eq. 3), regression equation (Eq.6) from present study and Brown’s method (Eq. 2) were used in place of Dendy (1974) and Heinemann (1981) methods due to their limitations of being applicable to only small reservoirs. The trap efficiency estimated using the above selected coarse grained sediment equations have been reported in Table 1. −0.8 ⎛C ⎞ 8005 − 35 × ⎜ ⎟ ⎝I ⎠ Te = (6) − 0 .1 ⎛C ⎞ 78.85 + ⎜ ⎟ ⎝I ⎠ The capacity and average annual inflow into the reservoir from 1963-2003 has also been presented in Table 1. From the table it can be seen that capacity of reservoir is reducing as inflow is showing an increasing trend. The Te estimated using mass balance equation (given as Eq. 1) is shown in Table 1. It is found that during first two years the observed Te is very less but after the third observation Te is reducing from higher values to lower with a slight variation. All the estimated methods have also shown the decreasing trend of Te. The initial two data sets have not been captured by any of the methods. Earlier the Te by Brown method has been estimated using κ = 1.0 for large reservoirs, as suggested by Gill (1979). But it was found that the value κ = 1.0 is overestimating Te. Hence, the constant κ was modified to κ = 0.58 (this value is equal to the average measured C/I ratio) and the Te was estimated by using Brown’s method (Table 1). This estimated Te closely followed the observed Te. Hence, it may be concluded that for the present study area (Gobindsagar reservoir), κ = 0.58 is an appropriate value in Brown’s method. The comparative plot of all the above methods and observed trap efficiency is also shown in Fig.6. The trend of the observed Te is showing a decreasing trap efficiency which is modelled well by all the methods. Brown’s approach worked well with κ = 0.58 for the present study area. The Gill’s method, which is based on Brune’s work, estimated better till 1977, but immediately after 1977, this method started underestimating the Te. This may be because of the range of capacity inflow ratio (C/I) changes International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
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from larger to medium reservoir condition during this period. This disadvantage is overcome by the equation deduced in the present study. The regression equation developed in the present study for Brune’s primarily highly flocculated and coarse grained sediments envelope curve performed better than any other method, as the estimated trend is quite close to the observed trend (as shown in Fig. 6). Hence, based on the present study, it may be concluded that the Brown’s method with κ = 0.58 and Brune’s regression equation developed in this study may be used for estimating trap efficiency of a Gobindsagar reservoir. Table 1 Comparison of observed and estimated trap efficiencies Year
Capacity (measured) (C, in x 106 m3)
C/I Ratio (observed)
Observed Te (%) (Eq. 1)
1963 1964 1967 1968 1969 1971 1972 1973 1974 1975 1976 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003
9,867.84 9,724.54 9,556.01 9,523.55 9,507.11 9,447.57 9,417.72 9,395.39 9,341.83 9,320.95 9,285.25 9,259.83 9,160.32 9,091.14 9,034.62 9,007.28 8,932.94 8,855.71 8,736.25 8,681.57 8,585.29 8,533.49 8,477.62 8,385.58 8,321.76
0.651 0.641 0.630 0.628 0.627 0.623 0.621 0.620 0.616 0.615 0.613 0.613 0.588 0.579 0.566 0.560 0.550 0.536 0.522 0.513 0.504 0.497 0.491 0.487 0.485
98.40 98.40 99.80 99.70 99.50 99.00 99.60 99.00 99.60 99.40 99.50 99.30 99.80 99.60 99.50 99.10 99.40 99.50 99.60 99.40 99.50 99.30 99.20 99.50 99.40
Coarse-grained sediment Brown (%) Gill (Eq. 2) (%) (Eq. 3) ( κ = 0.58) ( κ = 1.0) 99.73 99.53 99.56 99.72 99.52 99.55 99.72 99.52 99.53 99.72 99.51 99.53 99.72 99.51 99.53 99.71 99.51 99.52 99.71 99.51 99.52 99.71 99.51 99.52 99.71 99.50 99.51 99.71 99.50 99.51 99.71 99.50 99.50 99.71 99.50 99.50 99.71 99.49 99.46 99.70 99.49 99.44 99.70 99.49 99.42 99.70 99.49 99.41 99.70 99.48 99.39 99.70 99.48 99.36 99.69 99.47 99.33 99.69 99.47 99.31 99.69 99.46 99.28 99.68 99.46 99.27 99.68 99.45 99.25 99.68 99.45 99.24 99.68 99.44 99.24
Present study (%) (Eq. 6) 99.58 99.57 99.56 99.56 99.55 99.55 99.55 99.55 99.54 99.54 99.54 99.54 99.51 99.50 99.49 99.48 99.47 99.45 99.43 99.42 99.40 99.39 99.38 99.38 99.38
Fig. 6 Comparative plot of Te estimation with different methods - 82 -
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5 Conclusions The sediment trap efficiency (Te) of Gobindsagar Reservoir (Bhakra Dam) on Satluj River in Bilaspur district, Himachal Pradesh, in foothill of the Himalaya, India has been estimated using Brune’s and Brown’s methods. For estimating Te using Brune’s method, new regression equation was developed for Brune’s primarily highly flocculated and coarse grained sediments envelope curve. The estimated Te was compared with the measured Te as well as Gill’s approach. It was found that the trend of results closely follow the Brune’s coarse sediment envelope curve which shows that the sediments in this particular reservoir are mainly coarse sediments in nature. Finally, the results were compared with solution of Brown’s method with κ = 1.0 and it was found that Brown’s method overestimated the value of Te. Hence, the constant κ was modified to κ = 0.58 (average of the observed C/I values). The modified Brown’s method ( κ = 0.58) and present study regression equation have been found to be best suited for estimating the trap efficiency in the present study area, Gobindsagar Reservoir. It was also found that, the major advantage of these empirical methods was to give fairly reasonable results from very limited data: storage volume, average annual inflow and catchment area. As a limitation, the methods are applicable only to long-term average conditions. In the country like India where the sediment inflow and outflow are not usually measured, these empirical approaches are the best suitable approach to estimate sediment retention. Acknowledgements The authors would like to thank Dr. Sanjay Kr. Jain and Mr. Ajanta Goswami for the help extended. We gratefully acknowledge the anonymous reviewers and editors for their reviews and suggestions. Reference Arora P. K. and Goel M. P. 1994, Estimating life of a reservoir. Proc. of Workshop on Reservoir Sedimentation, Mysore (Karnataka) May 17-19, pp. 4-11. BBMB (Bhakra Beas Management Board) 2003, Sedimentation Survey Report. Bhakra Dam Circle, Nangal, India Brown C. B. 1944, Discussion of sedimentation in reservoirs. Ed. J. Witzig. Proc. of the American Society of Civil Engineers, Vol. 69, pp. 1493-1500. Brune G. M. 1953, Trap efficiency of reservoirs. Trans. Am. Geophysical Union, Vol. 34, No. 3, pp. 407-418. Campos R. 2001, Three-dimensional reservoir sedimentation model. PhD Thesis, Department of Civil Engineering, University of Newcastle, Newcastle. Churchill M. A. 1948, Discussion of analysis and use of reservoir sedimentation data. Ed. L. C. Gottschalk, Proc. of Federal Interagency Sedimentation Conference, Denver, pp. 139-140. Dendy F. E. 1974, Sediment trap efficiency of small reservoirs. Trans. of ASAE, Vol. 17, No. 5, pp. 898-988. Durbude G. D. and Purandara B. K. 2005, Assessment of sedimentation in the Linganmakki Reservoir using remote sensing. Journal of the Indian Society of Remote Sensing (Photonirvachak), Vol. 33, No. 4, pp. 503-510. Gill M. A. 1979, Sedimentation and useful life of reservoirs. Journal of Hydrology, Vol. 44, pp. 89-95. Heinemann H. G. 1981, A new sediment trap efficiency curve for small reservoirs. Water Resources Bulletin, Vol. 17, No. 5, pp. 825-830. Jain S. K., Singh P., and Seth S. M. 2002, Assessment of sedimentation in Bhakra Reservoir in the western Himalayan Region using remotely sensed data. Hydrological Sciences Journal, Vol. 47, No.2, pp. 203-212. Lagwankar V. G., Gorde A. K., and Patil K. D. 1994, Trends in reservoir sedimentation in India and abroad. Proc. of Workshop on Reservoir Sedimentation, Mysore (Karnataka) May 17-19, pp. 127-134. Morris G. L. and Fan J. 1998, Reservoir Sedimentation Handbook, McGraw Hill, New York, USA. Murthy B. N. 1980, Life of reservoir. Technical Report No. 19, Central Board of Irrigation and Power (CBIP), New Delhi. Shangale A. K. 1991, Reservoir Sedimentation Status in India, Jalvigyan Sameeksha, INCOH, NIH Roorkee, VI (1&2). Sharma P. D., Goel A. K., and Minhas, R. S. 1991, Water and sediment yields into the Satluj River from the high Himalaya. Mountain Res. Devel. Vol. 11, No. 2, pp. 87–100. Singh P. and Kumar N. 1997, Effect of orography on precipitation in the western Himalayan Region. Journal of Hydrology, Vol. 199, pp.183–206. Siyam A. M., Mirghani M., El zein S., Golla S., and El-sayed S. M. 2005, Assessment of the current state of the Nile basin reservoir sedimentation problems. NBCN-RE (Nile Basin Capacity Building Network for River Engineering), River Morphology, Research Cluster, Group–I Report. USACE (U.S. Army Corps of Engineers) 1989, Engineering and design: Sedimentation investigations of rivers and reservoirs. Engineering Manual 1110-2-4000, Washington, D.C. International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84
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Verstraeten G. and Posen J. 2000, Estimating trap efficiency of small reservoirs and ponds: Methods and implications for the assessment of sediment yield. Progress in Physical Geography, Vol. 24, pp. 219-251. Yang C. T. 1996, Sediment transport: Theory and Practice, McGraw-Hill, New York. Yang Xiaoqing. 2003, Manual on sediment management and measurement, World Meteorological Organization, Operational Hydrology Report No. 47, WMO-No. 948, Secretariat of the World Meteorological Organization–Geneva, Switzerland.
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