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Assessment of reservoir sedimentation for environmental planning in Rajaval reservoir of Gujarat (India).


The soil erosion, movement and deposition are part of natural hydrological processes. But, the rate of sedimentation in reservoirs and lakes is accelerated due to environmental degradation, lack of conservation measures in catchment, change in land use, deforestation, urbanization and industrialization. It is generally assumed that land use & land cover changes have caused accelerated soil erosion and increased sediment yield (White and Maldonado, 1991 [1]; Morris, 1997 [2]). The land use/cover in tropical ecosystem is changing at an ever-increasing rate causing adverse impact on physical and ecological process. Presently, the large dams in India loosing 1.95 billion m3 capacity annually due to wide spread soil erosion. Reservoir surveys are necessary to get more realistic data/estimate regarding the rate of siltation and to provide reliable criteria for studying the implication of annual loss of storage over a definite period of time with particular reference to reduction of intended benefits in the form of irrigation potential, hydropower, flood absorption capacity and water supply for domestic and industrial uses etc; and periodic reallocation of available storage for various pool levels.

With the correct knowledge of the sedimentation processes going on in a reservoir, remedial measures can be undertaken well in advance and reservoir operation schedule can be planned for optimum utilisation and maintenance of ecosystem of reservoirs. The conventional methods of reservoir sedimentation are time consuming, costly, cumbersome and require lot off manpower, therefore cannot be used frequently. But using the synoptic and repetitive viewing capacity of remote sensing sensors withy the ability of image processing with Geographic Information System (GIS) makes this method economical, less time consuming and easy. The advantages of using remote sensing data are that it is highly cost effective, easy to use and requires lesser time in analysis as compared to conventional methods. Spatial, spectral and temporal attributes of remote sensing provide invaluable synoptic and timely information regarding the revised area after the occurrence of sedimentation and sediment distribution pattern in the reservoir.

White, 1978 [3] examined a variety of measuring techniques for determining reservoir surface areas extracted from Landsat MSS near-IR imageries of different scales and compared their accuracy with field data. He concluded that none of the measuring techniques used was able to measure the reservoir water spread with consistent accuracy and no reason was attributed. The ability to map and estimate water spread from satellite data is well understood, and different techniques such as visual interpretation of satellite imagery, density slicing, and digital classification of water bodies have been employed for the delineation of water bodies (Work and Gilmer, 1976 [4], Thiruvengadachari et al, 1980 [5]; Jain and Goel, 1993 [6], Goel and Jain, 1996 [7], Jaiswal et al, 2008 [8], Jain and Kothiyari, 2000 [9]). Suvit et al, 1988 [10] used digital techniques in which density slicing of Landsat MSS near-IR (0.8-1.1 [micro]m) data were used to extract the water spreads of the Ubolratana reservoir of five different dates. Mangond et al, 1985 [11] employed digital classification techniques to estimate the water spread of the Malaprabha reservoir on March 02, 1973 using Landsat MSS data and reported a discrepancy of 8.29 % from the actual water spread. This discrepancy was attributed to the probable misclassification of boundary pixels.

Study Area

The Rajaval reservoir is situated on river Rajaval in Palitana block of Bhavnagar district. The catchment area of Rajaval reservoir up to dam site is 287.00 sq km. The dam is 3.57 km long earthen dam with 170 m masonary spillway. The full reservoir level (FRL) and dead storage level (DSL) of the dam is 56.75 and 50.00 m respectively. The gross and dead storage capacity of the dam is 33.01 M. cum and 4.38 M. cum respectively. The Rajaval dam was first impounded in the year 1982 to cater irrigation and drinking water in Mandavada, Anida and Bakhalaka villages of Bhavnagar district. The base map of reservoir has been given in Fig. 1.


Data used

In order to cover the whole range of live storage of reservoir, seven IRS 1D/P6 scenes of Path 92 and Row 57 have been selected on the basis of reservoir levels and of cloud free condition. The dates, elevation of reservoir and satellite information have been given in Table 1. The original elevation-capacity table and results of silt survey-2000 have been used for assessment of present rate of siltation from the catchment and finalizing the major steps for conservation of fragile ecosystem of the region.


The basic principle of revised capacity estimation using remote sensing and GIS is that when the sedimentation occurred in a reservoir its water spread reduced with respect to its original area before impoundment and the revised water spreads at different levels can be computed with the help of image analysis technique of GIS software. In the present study, the digital image analysis has been carried out using Integrated Land and Water Information System (ILWIS 3.0). All images were geo- referenced with the help of index map/Survey of India topo-sheets, so that they can be overlaid and linked with the latitude and longitude and the geographical area also can be determined. In remote sensing technique, the transmittance characteristics of different objects recorded by sensors are used to distinguish various land uses on the earth surface.

In the visible region of the spectrum (0.4-0.7 [micro]m), the transmittance of water is significant and the absorption and reflectance are low. The reflectance of water in the visible region scarcely rises above 5%. The absorption of water rises rapidly in the near-IR where both, the reflectance and transmittance are low. The normalized difference water index (NDWI) is used to differentiate water pixels form others in digital image analysis method of remote sensing data and can be expressed as:

NDWI = [ GREEN - NIR / GREEN + NIR ] 1.0

The slicing operation of the NDWI images is carried out to extract the water pixels from the rest. The revised areas obtained from this operation may be used to estimate the revised volume between two consecutive elevations with the help of cone formula. In the cone formula, the volume of water (V) between two consecutive spread [A.sub.1] and [A.sub.2] can be expressed as:

V = h/3 ([A.sub.1] + [A.sub.2] + [square root of ([A.sub.1][A.sub.2])]) 2.0

Where, h is the height between two elevations.

The revised cumulative capacities have been obtained by adding the revised volumes between consecutive intervals. For comparison, the original cumulative capacities on corresponding stages of satellite pass have been obtained from the original elevation-capacity curve. The results obtained from this analysis have been compared with the 2000-silt survey to assess the impact of development and other detrimental activities in the reservoir catchment.

5.0 Results and Analysis

In the present study, NDWI followed by slicing methods of image classification has been used to differentiate the water pixels from other land uses. The Band -III images and the masked out water spread areas of some selected dates have been presented in Fig. 2. The satellite data at D.S.L. i.e. 50.00 m and at F.S.L., i.e. 56.75 m were not available. To compute revised spread area on these levels, a graph has been plotted between reservoir elevation and revised water spread area. A best fit line has been plotted and the revised water spread at 50.00 m and 56.75 m have been computed as 112.5 hectare and 646.1 hectare respectively.


The revised water spread area and corresponding volume at different elevations have been worked out and revised cumulative capacity and percentage loss in gross storage at different levels have been estimated and given in the Table 2 & 3. The graphical representation of the elevation v/s original and revised cumulative capacities of Rajaval reservoir has been presented in Fig. 3.

From the analysis of the results, it has been observed that 5.05 M. cum of gross storage and 4.63 M. cum of live storage have been lost in last 25 years (1982 to 2007). Assuming constant rate of sedimentation over a period of 25 years, the rate of silting may come out as 0.202 M. cum/year. As the rate of silting varies with the catchment area of reservoir, the silting rate in the common unit may be expressed as 7.05 ham/100 sq. km/year.


Environmental impact assessment

The satellite remote sensing data of different seasons have been analyzed for detection of land uses in the catchment of Rajaval reservoir. The major land uses found are agriculture, forest, waste land, water bodies and built up areas. The catchment is undulating and hills remain barren in most part of year except for few small areas with some trees and thorny scrubs on less assessable slopes. The sparse vegetative covers on sloppy land facilitate high runoff and excessive soil erosion. The agriculture lands on river banks are constantly suffering from sheet erosion and thus loosing valuable nutrients making these lands unproductive.

For assessment of impact of soil erosion, the results of the silt survey of the reservoir in the year 2000 has been used. This survey was carried out using conventional method in which revised gross storage at FSL has been computed as 30.21 M. cum. The rate of sedimentation for two evaluation periods has been worked out and siltation pattern at different levels of reservoir has been presented in Table 4 and 5. If we compare these two periods separately as impoundment to 2000 (Period I) and 2000 to 2007 (Period II), the rate of siltation in Rajaval reservoir was 0.156 M. cum/year during period I and increased to 0.322 M. cum/year during period II. The silting rate during Period II has doubled and deposition of sediment in upper part of reservoir increased significantly indicated wide spread erosion and environmental degradation in the catchment. Therefore, it is needed to implement the soil conservation measures in the catchment of Rajaval reservoir at the earliest to preserve the ecosystem of the region.

Suggested measures

The corrective measures for reduction of soil erosion from the catchment are essential for protection of fragile ecosystem of Rajaval reservoir. This will also helpful to maintain the desired supply from reservoir for its designated uses. Following measures may be used to combat the erosion linked environment degradation in the Rajaval reservoir:

* A well planned integrated land use policy should be developed to guide management of agriculture and forest lands scientifically and sustainably.

* The land use plan for the area should be developed considering the land capability of different units in the centre.

* The site specific conservation plan considering the needs and limitations of each land units should be developed.

* The contour farming, contour bunding, mulching, rotation of crops, mixed cropping in the agriculture lands in plains and terracing should be used in sloppy lands.

* The afforestation of waste lands and forest land should be done extensively.

* A systematic monitoring network should be developed in agricultural lands to assess the balance between input and withdrawal of nutrients to guards against possible nutrients depletion (Sarkar et al, 1991 [12]).

* The developmental activities and urbanization in the region are creating pressure on delicate ecosystem. An Area-wide Environment Quality Management (AEQM) approach needs to be adopted.

* Education and training of soil and water conservation measures would enable the local resident for adapting the practices according to changed scenario and develop awareness about the environment.


Reduction in the storage capacity beyond a limit prevents the reservoir from fulfillment of the purpose for which it is designed. Periodical capacity surveys of reservoir help in assessing the rate of sedimentation and reduction in storage capacity. In the recent past, satellite remote sensing has emerged as an important tool in carrying out reservoir capacity surveys rapidly, frequently and economically. The Rajaval reservoir is an important reservoir of Bhavnagar district of Gujarat has lost 5.043 M. cum of gross storage. The silting rate of Rajaval reservoir in common unit is computed to be 7.05 ha-m/100 [km.sup.2]/year. It may be observed from the analysis that the loss in cumulative capacity at DSL and FRL are 9.67 % and 15.28% respectively which may be due to higher siltation in the live storage zone of the reservoir. The revised capacity table/curve obtained in the analysis may be used in future reservoir operation. The rate of siltation in Rajaval reservoir increased to double in the last seven years, which may be due to rapid development, change in land uses, deforestation, erosion etc. Therefore, it is necessary to take suggested conservation measures and development of catchment area treatment plan to control soil erosion from the catchment of Rajaval reservoir.


[1] White, S. and Maldonado, F., 1991, "The Use & Conservation of National Resources in Andes of Southern Ecuador, Mountain Research and Development", 11, 37-55.

[2] Morris, A., 1997, "Afforestation Projects in Highland Ecuador: Pattern of Success and Failure, Mountain Research and Development", 17, 31-42.

[3] White, M.E., 1978, "Reservoir Surface Area from Landsat Imagery", Journal of Photogrammatric Engineering and Remote Sensing, 11, 1421-1426.

[4] Work, E.A. Jr. and Gilmer, D.S., 1976, "Utilization of Satellite Data for Inventorying Prairie Ponds and Lakes'', Photogrammetric Engineering & Remote Sensing, 42, 685-694.

[5] Thiruvengadachari, S., Subba Rao, P. and Rao, K.R., 1980, "Surface Water Iinventory through Satellite Sensing", Journal of WRPM, 106, 493-502.

[6] Jain, S. K. and Goel, M. K., 1993, "Reservoir Sedimentation using Digital Image Processing of IRS-I, LISS-I data", Proc. of National Symposium on Remote Sensing Applications for Resource Management with Special Emphasis on N.E. Region, Guwahati (India), 504 - 510.

[7] Goel, M. K. and Jain, S. K., 1996, "Evaluation of Reservoir Sedimentation using Multi-Temporal IRS-1A LISS-II Data", Asian-Pacific Remote Sensing and GIS Journal, 8(2), 3-43.

[8] Jaiswal, R. K., Thomas, T., Singh, S. and Galkate, R.V., 2008, "Assessment of Revised Capacity of Kharo Reservoir using Remote Sensing and GIS", Proc. of National Seminar on Conservation and Restoration of Lakes (CAROL-2008), Nagpur (India), 551-562.

[9] Jain, M.K. and Kothiyari, U.C., 2000, "Estimation of Soil Erosion and Sediment Yield using GIS", Hydrological Sciences Journal, 45(5), 771-786.

[10] Suvit, V., Srisrngthong, D., Thisayakorn, K., Suwanwerakamtorn, R., Wongparn, S., Rodprom, C., Leelitham, S., and Jittanon, W, 1988, "The Reservoir Capacity of Ubolratana Dam Between 173 and 180 Meters above Mean Sea Level", Asian-Pacific Remote Sensing and GIS Journal, 1(1), 1-6.

[11] Managond, M.K., Alasingrachar, M.A. and Srinivas, M.G., 1985, "Storage Analysis of Malaprabha Reservoir using Remotely Sensed Data", Nineteenth International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan, 749-756.

[12] Sarkar, M.C., Banerjee, N.K., Rana, D.S. and Uppal, K.S., 1991, "Field Measurement of Ammonia Volatilization Losses of Nitrogen from Urea Applied to Wheat", Fertilizers News, 36(11), 25-29.

R. K Jaiswal (1) * T. Thomas (1) and R.V. Galkate (1)

(1) Scientist, National Institute of Hydrology, GPSRC, 567, Manorama Colony, Sagar (M.P.), India

* Corresponding Author E-mail-
Table 1: Satellite data used for estimation of water spread area.

S. N. Date of Pass Elevation (meters) Satellite Sensor

1 12-May-2007 46.57 IRS 1-D LISS-3
2 17-April-2007 48.52 IRS 1-D LISS-3
3 29-April-2006 49.52 IRS P-6 LISS-3
4 23-March-2007 51.32 IRS 1-D LISS-3
5 26-Feburary-2007 52.87 IRS 1-D LISS-3
6 16-Feburary-2006 54.02 IRS P-6 LISS-3
7 14-October-2006 55.32 IRS P-6 LISS-3

Table 2: Revised water spread and percentage loss in volume between
successive elevations.

Date of Pass Reservoir Elevation Number of Revised Area
 (meter) Water Pixel (M Sqm)

River Bed 39.45 0.00

DSL * 50.00 1.125

12-May-07 50.05 2072 1.144

17-Apr-07 51.60 3141 1.735

29-Apr-06 52.05 4035 2.228

23-Mar-07 52.60 4756 2.627

26-Feb-07 53.80 6494 3.586

16-Feb-06 55.05 10114 5.585

14-Oct-06 56.60 11304 6.243

FSL * 56.75 11700 6.461

Date of Pass Revised Volume Original Cumu. Original Volume
 (M cu.m) Capacity (M cu.m) (M cu.m)

River Bed 0.000
 3.958 4.382
DSL * 4.382
 0.057 0.068
12-May-07 4.450
 2.215 3.289
17-Apr-07 7.739
 0.889 1.100
29-Apr-06 8.839
 1.334 1.595
23-Mar-07 10.434
 3.713 4.866
26-Feb-07 15.300
 5.686 6.092
16-Feb-06 21.392
 9.162 9.368
14-Oct-06 30.760
 0.953 2.250
FSL * 33.010

Date of Pass Loss in Volume % Loss in Volume
 (M cu.m) between Successive

River Bed
 0.424 9.67
 0.011 16.57
 1.074 32.64
 0.211 19.15
 0.261 16.39
 1.153 23.70
 0.406 6.66
 0.206 2.20
 1.297 57.66

Table 3: Estimation of Loss in Storage of Rajaval Reservoir.

Date of Pass Reservoir Original Capacity
 Elevation (meter) (M cu. m)

 Volume Cumulative

River Bed 39.45 0.000
DSL * 50.00 4.382 4.382
12-May-07 50.05 0.068 4.450
17-Apr-07 51.60 3.289 7.739
29-Apr-06 52.05 1.100 8.839
23-Mar-07 52.60 1.595 10.434
26-Feb-07 53.80 4.866 15.300
16-Feb-06 55.05 6.092 21.392
14-Oct-06 56.60 9.368 30.760
FSL * 56.75 2.250 33.010

Date of Pass Revised Capacity Loss in Cum.
 (M cu. m) Capacity
 (M cu.m)
 Volume Cumulative Capacity

River Bed 0.000
DSL * 3.958 3.958 0.424
12-May-07 0.057 4.015 0.435
17-Apr-07 2.215 6.230 1.509
29-Apr-06 0.889 7.120 1.719
23-Mar-07 1.334 8.453 1.981
26-Feb-07 3.713 12.166 3.134
16-Feb-06 5.686 17.852 3.540
14-Oct-06 9.162 27.014 3.746
FSL * 0.953 27.967 5.043

Date of Pass % Loss in

River Bed
DSL * 9.67
12-May-07 9.78
17-Apr-07 19.50
29-Apr-06 19.45
23-Mar-07 18.98
26-Feb-07 20.48
16-Feb-06 16.55
14-Oct-06 12.18
FSL * 15.28

Table 4: Comparison of revised capacities in Rajaval reservoir.

S. R.L. Cumulative capacity (M. cum) Loss in cumulative
N. (m) capacity (M. cum)

 Original Revised Revised 1982 to 2000 to
 (1982) (2000) (2007) 2000 2007

1. 39.45 0.00 0.00 0.00 - -
2. 50.00 4.38 4.38 3.96 0.00 0.43
3. 50.50 5.11 5.03 4.59 0.07 0.44
4. 51.00 6.27 5.78 5.33 0.49 0.45
5. 51.50 7.49 6.67 6.20 0.82 0.48
6. 52.00 8.72 7.72 7.20 1.00 0.52
7. 52.50 10.03 8.92 8.37 1.11 0.55
8. 53.00 12.06 10.26 9.69 1.79 0.58
9. 53.50 14.08 11.98 11.32 2.10 0.66
10. 54.00 16.11 13.98 13.19 2.13 0.80
11. 54.50 18.23 16.27 15.28 1.96 1.00
12. 55.00 21.11 18.75 17.61 2.35 1.14
13. 55.50 24.05 21.58 20.21 2.47 1.37
14. 56.00 27.57 24.65 23.09 2.92 1.57
15. 56.50 30.73 28.23 26.26 2.89 1.97
16. 56.75 33.01 30.22 27.96 2.79 2.26

S. Average yearly
N. siltation between
 successive level (M.
 1982 to 2000 to
 2000 2007

1. - -
2. 0.00 0.06
3. 0.00 0.00
4. 0.02 0.00
5. 0.02 0.00
6. 0.01 0.01
7. 0.01 0.00
8. 0.04 0.00
9. 0.02 0.01
10. 0.00 0.02
11. -0.01 0.03
12. 0.02 0.02
13. 0.01 0.03
14. 0.03 0.03
15. 0.00 0.06
16. -0.01 0.04

Table 5: Analysis of silting pattern in Rajaval reservoir.

S.N. Particulars Rajaval

1. Year of impoundment 1982
2. Original Dead storage (M. cum) 4.382
3. Original Gross storage (M. cum) 33.01
4. Original Live storage 28.628

Period I (From impoundment to 2000)

5. Dead storage in M. cum (2000 silt survey) 4.38
6. Live storage in M. cum (2000 silt survey) 25.84
7. Gross storage in M. cum (2000 silt survey) 30.22
8. Change in gross storage (M. cum) 2.80
9. Rate of siltation (M. cum /year) 0.156
10. Rate of siltation (M. cum /100sq km/year) 0.054

Period II (From 2000 to 2007)

12. Dead storage in M. cum (2007RS survey) 3.96
13. Live storage in M. cum (2007 RS survey) 23.99
14. Gross storage in M. cum (2007 RS survey) 27.96
15. Change in gross storage (M. cum) 2.265
16. Rate of siltation (M. cum /year) 0.322
17. Rate of siltation (M. cum /100sq km/year) 0.112

Average pattern (From impoundment to 2007)

19. Change in gross storage (M. cum) 5.05
20. Rate of siltation (M. cum /year) 0.202
21. Rate of siltation (M. cum /100sq km/year) 0.071
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Author:Jaiswal, R.K.; Thomas, T.; Galkate, R.V.
Publication:International Journal of Applied Environmental Sciences
Geographic Code:9INDI
Date:Sep 1, 2010
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