SEDIMENT TRANSPORT AND RESERVOIR SEDIMENTATION
W. Saenyi, H. HolzmannInstitute of Water Management, Hydrology and Hydraulic Engineering, University of Agricultural Sciences (BOKU), Muthgasse 18, Vienna, Austria.
ABSTRACT
The Water Erosion Prediction Project (WEPP) model has been applied to the Masinga catchment in Kenya for the estimation of soil loss due to surface runoff resulting from intense tropical rainfall. The WEPP model is a distributed parameter continuous simulation model for predicting daily soil loss and deposition due to rainfall, snowmelt and irrigation. Most computer models for the simulation and prediction of sediment transport in rivers and reservoirs are one-dimensional. Although truly two- or three-dimensional models are available, they require extensive field data for calibration and may be difficult to apply. A semi-two-dimensional model for water and sediment routing, GSTARS 2.1, may be an alternative for solving river engineering problems. This paper provides a brief description of the systematic and integrated approach based on well established sediment transport equations and the Bureau of Reclamation’s Generalized Stream Tube model for Alluvial River Simulation (GSTARS 2.1) and its applications. Examples of computed results are presented to illustrate the applicability of different components of this approach.
Keywords water erosion, sediment transport, modelling
INTRODUCTION
As a result of runoff from rainfall, soil particles on the surface of a watershed can be eroded and transported through the processes of sheet, rill and gully erosion. Once eroded, sediment particles are transported through a river system and are eventually deposited in a reservoir, lake or at sea. Therefore, an erosion model capable of predicting surface erosion and routing sediment through a channel system is desirable.
Engineering techniques used for the determination of reservoir sedimentation processes rely mainly on field surveys. Field surveys can be used for the determination of what has happened but not for predictive purposes.
During the 1997 Congress of the International Commission on Large Dams (ICOLD), the Sedimentation Committee passed a resolution encouraging all member countries to (a) develop and apply methods for the prediction of the rate of surface erosion based on rainfall and soil properties, and (b) develop and apply computer models for the simulation and prediction of reservoir sedimentation processes. This paper provides a brief description of an ongoing study in compliance with the above two ICOLD resolutions. Preliminary results will be presented to demonstrate the feasibility of a systematic and rational approach for the determination of surface erosion rates and sediment transport in rivers. In addition, the application of a reservoir sedimentation model is presented.
WATER EROSION AND SEDIMENT ROUTING THROUGH CHANNELS
Soil erosion and sediment yield modelling
The Water Erosion Prediction Project (WEPP) model current version (v2001.3) was released in April 2001 (Flanagan et al., 2001). The model represents erosion prediction technology based on fundamentals of infiltration theory, hydrology, soil physics, plant science, hydraulics, and erosion mechanics. The model provides several major advantages over existing erosion prediction technology: (a) its capabilities for estimating spatial and temporal distributions of soil loss - the net soil loss for an entire hillslope or
for each point on a slope profile can be estimated on daily, monthly, or average annual basis; and (b) since the model is process-based, it can be applied on a broad range of conditions that may not be practical or economical when field tests are used.
The model can be used in both hillslope and watershed applications. Runoff characteristics, soil loss and deposition are first calculated on each hillslope with the hillslope component of WEPP for the entire simulation period. Main results are saved in a pass file that is used during the watershed routing.
Then the model combines simulation results from each hillslope and performs runoff and sediment routing through the channels and impoundments each time runoff is produced on one of the hillslopes or channels, or if there is an outflow from one of the impoundments. In this study, no impoundments are considered.
The first step in the application of WEPP on the Masinga catchment (7,335 km2) for sediment yield prediction was to sub-divide it into subcatchments using the available topographic map. Each subcatchment was further subdivided into representative hillslopes. A total of 31 representative hillslopes were identified (Fig 1). Eight of the 31 hillslopes drain into the Thika arm of the Masinga reservoir while the remaining 23 drain into the Tana arm (Fig 4a).
WEPP can give an output of over 100 variables. In this paper only a few were selected for presentation (Saenyi, 2002). These included daily values of sediment yield, runoff, evapotranspiration and total soil water.
Sediment yield from the WEPP watershed simulation was correlated to discharge by the least squares method to obtain the sediment discharge rating curves used in GSTARS 2.1 as:
Thika arm: Qs =0.89Q1.707, r = 0.85 n = 107 (1)
Tana arm: Qs =0.52Q0.887, r = 0.79 n = 153 (2)
where Qs = sediment discharge (metric tonne/day), Q = water discharge (m3/s) r = correlation coefficient, n = number of data points.
Sediment transport in river channels
Since there is a high concentration of wash load in the flow entering the Masinga reservoir (Bobotti, 1998), its effect on sediment fall velocity, flow viscosity, and relative specific weight of sediment is significant.
Hence, a modified sediment transport function for sediment-laden flow with high concentration of wash load (Yang et al., 1996) was used for sediment transport computations in the river channels.
RESERVOIR SEDIMENTATION SIMULATION USING GSTARS 2.1 MODEL
The generalized Stream Tube model for Alluvial River Simulation (GSTARS) was first released by the US Bureau of Reclamation in 1986 (Molinas and Yang, 1986) for CYBER mainframe computer application.
A revised and enhanced model GSTARS version 2.0 (GSTARS 2.0) was released by Yang et al. (1998).
The most recent version, GSTARS 2.1, was released by Yang and Simoes (2000).
With proper selection of a sediment transport function, GSTARS 2.1 can be applied to a wide range of sediment conditions with particle sizes ranging from clay, silt, sand, to gravel. GSTARS 2.1 also has the ability to consider the effects of wash load on the sediment transport rate by using the modified unit stream power formula proposed by Yang et al. (1996). An earlier version, GSTARS 2.0, was applied to the Willow Creek emergency spillway to demonstrate its capabilities in predicting the sediment transport and channel forming processes downstream (Yang et al., 1998). In this study, Yang’s (1973) and (1979) formulae were used. It was shown that both channel width and depth can change during the channel forming process (Yang et al., 1998).
Fig 1: Location of the Masinga catchment and reservoir.
CALIBRATION PROCESS AND DIFFICULTIES ENCOUNTERED
The first step in the application of GSTARS 2.1 to the Masinga reservoir was to calibrate the model.
The following parameters were adjusted to fit the simulated cross-sections to the measured cross-sectional data:
- shear threshold for deposition of clay and silt (STDEP), used to determine the initial condition for deposition,
- shear threshold for particle erosion of clay and silt (STMERO), - slope of the erosion rate curve for mass erosion (ERMASS), - size gradation distribution of the incoming sediment.
To calibrate GSTARS 2.1, first the size gradation distribution of incoming sediment was varied and predicted reservoir cross-sections were obtained. The predicted cross-sections and thalweg were then compared with measured ones to establish whether the two sets matched. The reason for varying the size gradation distribution of incoming sediment was to distribute sediment deposits longitudinally in such a way that predicted and measured cross-sections and thalweg approximately matched. Normally, the bed load and the coarser fraction of the suspended load are deposited at the mouth of the reservoir, while fine sediments with lower settling velocities are transported and deposited deeper into the reservoir or near the dam wall.
Some problems were encountered while calibrating the model. It was difficult to adjust the model parameters so that the simulated and observed cross-sections matched for all the stations. For instance, there was a bigger deviation between the measured and observed cross-sections for the sections around the confluence.
This could be attributed to the formation of eddy and/or secondary currents at the confluence of the two arms (Thika and Tana). Since GSTARS 2.1 is based on a stream tube concept, the presence of secondary and eddy currents may cause the model to fail. It was also found that there was a big deviation between simulated and observed data for cross-sections near the dam wall. This could be attributed to backwater flow and the
formation of eddy currents. Otherwise, for other cross-sections, the observed and simulated bed elevation changes were in good agreement.
GSTARS 2.1 SIMULATED RESULTS
Figs 2a, 2b, and 3 demonstrate GSTARS 2.1 capabilities in computing the transverse and longitudinal bed profile of a reservoir due to sedimentation. Yang’s (1996) modified formula was employed in the Masinga reservoir sedimentation study because of the presence of a high concentration of wash load in the flow.
Since most of the sediments entering the Masinga reservoir are fine grained silt and clay, the cohesive sediment part of GSTARS 2.1 was used in conjunction with the non-equilibrium sediment transport option.
The processing and visualization of GSTARS 2.1 results were performed using a two-dimensional BOSS SMS model (1996) to yield the sediment distribution in the reservoir (Figs 4b,c). Changes from Fig 4b to Fig 4c show that the model is capable of simulating the sedimentation process in the reservoir. From the figures, it can be seen that the reservoir bathymetry has been altered drastically due to siltation.
Fig 2a: Predicted channel development compared with a measured cross-section in 1988, for a station 10 km upstream of Masinga dam.
Fig 2b: Predicted channel development compared with a measured cross-section in 1988, for a station 5 km upstream of Masinga dam.
T h alw eg (T h iK a arm )
1010.0 1015.0 1020.0 1025.0 1030.0 1035.0 1040.0 1045.0 1050.0 1055.0
15.1 14.7 14.1 13.7 12.9 12.2 11.5 11.1 10.4 9.7 9.1 8.3 7.3 6.7 5.9 5.4 4.7 3.7 2.6 2.2 2.0 1.0 0.0
D istance (K m )
Elevation of bed (m)
Initial,1981 M easured,1988 S im ulated,1988
Fig 3: Thalweg from the mouth of the reservoir to station 29.9 km upstream of Masinga dam.
(a) (b)
(c)
Fig 4: (a) Masinga reservoir showing the location of cross-sections. (b) Initial bathymetry of the reservoir in 1981. (c) Bathymetry of the reservoir in 1988.
SUMMARY AND CONCLUSION
The results from WEPP watershed simulations were used to derive sediment rating curves for both Tana and Thika subcatchments. These curves were then used as input into the sedimentation model for describing suspended load inflow as a function of water discharge. The focus of the sedimentation modelling was to see how the reservoir bed elevations change after 20 years of reservoir operation. From the plotted cross sections, it was found that most deposition occurred along the thalweg with deposition depths typically in the range of 1.5 m to 3 m. Most of the sediment delivered to the reservoir was deposited along the main channel and a smaller fraction on the reservoir terraces. The model predicted highest sedimentation at the mouth of the reservoir, at the confluence of the two arms (Tana and Thika), and near the dam wall.
ACKNOWLEDGEMENTS
This ongoing study is partly supported by Austrian Academic Exchange Service (ÖAD), and the Institute of Water management, Hydrology and Hydraulic Engineering of the University of Agricultural Sciences, BOKU, Vienna.
Masinga dam
REFERENCES
Bobotti, O.K. (1998) Masinga Reservoir Capacity and Sedimentation, Kenya. A World Bank Project; Tana and Athi Rivers Development Authority, TARDA Report, Vol. 2: Data Annexes.
BOSS SMS model (1996) BOSS SMS Computer program user’s manual version 4.3. Engineering Computer Laboratory, Brigham Young University.
Flanagan, D.C., Laflen, J.M., Lane, L.G., Nearing, M.A. (2001) Water Erosion Prediction Project (WEPP ) Computer Program (version 2001.3) for Windows. USDA-Water erosion prediction project:
Hillslope profile and watershed model documentation. NSERL Report No. 10, National Soil Erosion Research Laboratory, USDA-ARS-MWA, West Lafayette, Indiana.
Molinas, A., Yang, C.T. (1986) Computer program users manual for GSTARS. Technical report, U.S.
Department of Interior Bureau of Reclamation Engineering and Research Centre, Denver, Colo.
Saenyi, W.W. (2002) Sediment Management in Masinga Reservoir, Kenya. A Ph.D thesis submitted to the University of Agricultural Sciences, BOKU; Institute of Water Management, Hydrology and Hydraulic Engineering Vienna, Austria.
Yang, C.T. (1973) Incipient motion and sediment transport. J. of the Hydr. Div. ASCE, 99(HY10).
Yang, C.T. (1979) Unit stream power equations for total load. J. of Hydrology, 40.
Yang, C.T. (1984) Unit stream power equation for gravel. J. of the Hydr. Div. ASCE, 110(HY12).
Yang, C.T. (1996) Sediment transport: theory and practice. McGraw-Hill Companies, Inc., New York, NY.
Yang, C.T., Molinas, A., and Wu, B. (1996) Sediment transport in the Yellow River. J. of Hydr.
Engineering, ASCE, 122, 5.
Yang, C.T, Randle, T.J., Hsu, S.K. (1998) Surface erosion, sediment transport, and reservoir sedimentation.
In Summer, W., Klaghofer, E., and Zhang, W., editors, IAHS Publication no. 249. 3-12.
Yang, C.T., Trevino, M.A., Simoes, F.J.M. (1998) User’s manual for GSTARS 2.0 (Generalized Stream Tube model for Alluvial River Simulation version 2.0). U.S. Bureau of Reclamation, Technical Service Center, Denver, Cororado.
Yang, C.T., Simoes, F.J.M. (2000) User’s manual for GSTARS 2.1 (Generalized Stream Tube model for Alluvial River Simulation version 2.1). U.S. Bureau of Reclamation, Technical Service Center, Denver, Cororado.