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Sinnakaudan, S. K.

Sulaiman, M. S.

English

Hydraulic Engineering Repository

Conference Paper, Published VersionSinnakaudan, S. K.; Sulaiman, M. S.The Sediment Transport Prediction For Mountain RiversZur Verfügung gestellt in Kooperation mit/Provided in Cooperation with:Kuratorium für Forschung im Küsteningenieurwesen (KFKI)Verfügbar unter/Available at: https://hdl.handle.net/20.500.11970/109885Vorgeschlagene Zitierweise/Suggested citation:Sinnakaudan, S. K.; Sulaiman, M. S. (2010): The Sediment Transport Prediction ForMountain Rivers. In: Sundar, V.; Srinivasan, K.; Murali, K.; Sudheer, K.P. (Hg.): ICHE 2010.Proceedings of the 9th International Conference on Hydro-Science & Engineering, August2-5, 2010, Chennai, India. Chennai: Indian Institute of Technology Madras.Standardnutzungsbedingungen/Terms of Use:Die Dokumente in HENRY stehen unter der Creative Commons Lizenz CC BY 4.0, sofern keine abweichendenNutzungsbedingungen getroffen wurden. Damit ist sowohl die kommerzielle Nutzung als auch das Teilen, dieWeiterbearbeitung und Speicherung erlaubt. Das Verwenden und das Bearbeiten stehen unter der Bedingung derNamensnennung. Im Einzelfall kann eine restriktivere Lizenz gelten; dann gelten abweichend von den obigenNutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.Documents in HENRY are made available under the Creative Commons License CC BY 4.0, if no other license isapplicable. Under CC BY 4.0 commercial use and sharing, remixing, transforming, and building upon the materialof the work is permitted. In some cases a different, more restrictive license may apply; if applicable the terms ofthe restrictive license will be binding.Proceedings of ICHE2010, IIT Madras, Aug 2-5,2010 The Sediment Transport Prediction for Mountain Rivers                          THE SEDIMENT TRANSPORT PREDICTION FOR MOUNTAIN RIVERS   Sinnakaudan S.K.1 and M.S. Sulaiman2                                              Abstract:  The phenomenon of sediment transport has been studied for the past 100 years and always given special attention by the researchers and engineers. The human activities severely affected by the sedimentation problem such as flooding, deposition, and the malfunction of hydraulic structure. Various studies had been conducted to quantify sediment transport pattern in Malaysia. However the studies which are focussed on mountain rivers are still lacking. Furthermore, there is no specific equation to predict the total bed material load transport for highland rivers in Malaysia. Thus, the present study set as a pilot project to derive such an equation. A comprehensive field sampling and laboratory analysis were carried out to obtain the sediment database. The governing parameter that has high influence on sediment transport was selected based on literature survey and field observation. These parameters were checked for its correlation with field data before developing the equation. The equation is developed by fitting the selected influential parameters into SPSS program. The Multiple Linear Regression Analyses (MLR) was employed to derive the equation. The multicolinearity effects were avoided which may reduce the efficiency of transport prediction. The performance of the existing equation was tested and validated using existing field data from rivers in Malaysia.  Keywords: sediment transport; mountain rivers; correlation; multiple linear regression analysis.  INTRODUCTION  The numerical equations for sediment transport were developed and verified over a decade by past researchers. Those developed equations exhibit different efficiency due to studied parameters, theoretical backgrounds, sampling techniques and mathematical approaches (Yang, 1996). Some of those approaches were commonly applied and validated namely shear stress approach (DuBoys, 1879), discharge approach (Schoklitsch, 1934), energy slope approach (Meyer-Peter and Muller, 1948), probabilistic approach (Einstein, 1942, 1950), stochastic approach (Yang, 1972), Multiple Linear Regression (MLR) approach (Bagnold, 1966, Sinnakaudan et al., 2006) and equal mobility approach (Parker, 1990). A good appraisal for past developed sediment transport equations are cited in Einstein (1950), Einstein modified by Colby                                                  1 Head, Water Resources Engineering and Management Research Centre (WAREM), Institute for Infrastructure Engineering & Sustainable Management (IIESM), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia, E-mail: drsshan@yahoo.com  (Member, IAHR). 2 Research Student, Water Resources Engineering and Management Research Centre (WAREM), Institute for Infrastructure Engineering & Sustainable Management (IIESM), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia, E-mail: mohd.sofiyan@yahoo.com  Proceedings of ninth International Conference on Hydro-Science and Engineering (ICHE 2010), IIT Madras, Chennai, India. 2 - 5  August 2010 1483Proceedings of ICHE2010, IIT Madras, Aug 2-5,2010 The Sediment Transport Prediction for Mountain Rivers                      & Hembree (1955), Egiazaroff (1965), Laursen (1958), Colby (1964), Bagnold (1966),  Engelund & Hansen (1967), Graf (1971), Toffaleti (1969),  Ackers & White (1973) and Yang (1972, 1984, 1996) and Sinnakaudan et al (2006).    Most of the past sediment transport equation were developed based on the data obtained from low to medium gradient river which have typical hydraulic characteristics such as  water surface slope less than 0.002m/m, sandy beds and in rivers with uniform sediment composition. The nonuniformity of bed material at the high gradient river shows distinct hydraulic differences as compared to low gradient river (Thompson, 2006). Thus, the applicability of existing equations for high gradient river sedimentation predictions is still questionable.  It is evident that since 1990s, a few attempts being made to quantify the sediment transport pattern at Malaysian river. Unfortunately, most of the research work were done at low to medium gradient rivers and mostly cited in Ariffin et al. (2002) and Sinnakaudan et al. (2006). The current development trend especially in Malaysia mountain river basin shows that excessive developments brought severe erosion and sedimentation problems while it was continuously being carried out without improper monitoring and observation (Sinnakaudan, 2006).   MALAYSIAN MOUNTAIN RIVER DATA  The sediment and hydraulic database were sampled at 22 high gradient  rivers which represent different morphological characteristics for mountain drainage basin. A total number of 55 genuine sediment and hydraulic data were collected at low to medium flow condition and summarized in Table 1.  Table  1. Range of sediment data Data groups Analysis data Validation data Discharge, Q (m3/s) 0.15-7.21 0.15-6.71 Velocity, V (m/s) 0.21-2.07 0.10-1.55 Width, B (m) 6.00-20.00 5.00-21.30 Flow Depthoy  (m) 0.15-1.28 0.13-1.41 Area, A (m2) 0.57-10.91 0.39-11.85 Hydraulic Radius, R (m) 0.11-0.94 3.01-21.71  Slope, So (m/m) 0.11-0.94 0.00-0.03 Sediment Size, d50 (mm) 2.00-147.43  2.00-157.81  Measured Total Load, Tj (kg/s) 1.00x10-3 - 0.12 0.90x10-4 - 1.63  INFLUENTIAL PARAMETERS FOR SEDIMENT TRANSPORT  The most important part in developing the new equation is to find the influential parameter that controls the sediment transport in high gradient rivers. These parameters are used as starting point to develop the numerical equation. The significant parameters controlling the sediment transport can be grouped into 7 categories namely mobility, transport, sediment, flow resistance, 1484Proceedings of ICHE2010, IIT Madras, Aug 2-5,2010 The Sediment Transport Prediction for Mountain Rivers                      Relationship Between Transport Parameter (Φ) and Mobility Parameter (F*)0.0000000010.00000010.000010.0010.1100.0001 0.001 0.01 0.1 1 10(F*)ΦRelationship Between Transport Parameter (Φ ) and Hiding-Exposure Function (ξ )1E-101E-081E-060.00010.011( Φ)conveyance shape, hiding exposure function and fractional function. The sensitivity analysis was tested to observe the trend between the sediment data and listed parameters (Figure 1). The correlation trend between dependent and independent variables were checked and verified using the Pearson correlation coefficient. The intensity of the correlation coefficient was proposed by Cohen (1988) as an indicator pattern of the sensitivity test (Table 2).  Table 2. Correlation Coefficients (Cohen (1988). Correlation Negative Positive Small −0.29 to −0.10 0.10 to 0.29 Medium −0.49 to −0.30 0.30 to 0.49 Large −1.00 to −0.50 0.50 to 1.00  The coefficient, r, is given as 211 121⎥⎥⎦⎤⎢⎢⎣⎡⎟⎠⎞⎜⎝⎛ −⎟⎠⎞⎜⎝⎛ −⎟⎠⎞⎜⎝⎛ −⎟⎠⎞⎜⎝⎛ −=∑ ∑∑= =−−=−−niniiiniiiyyxxyyxxr  The value of the correlation coefficient, r  lies between -1 to 1. The correlation is perfect if the r  = 1 or r  = -1. If 11 −≤≤ r  and 0≠r , there exist linear relationship between two variables. Cohen [21] suggest the high correlation coefficient value as 0.15.0 ±≤≤± r . The highly correlated parameters are shown as  ⎟⎟⎟⎟⎟⎟⎟⎟⎠⎞⎜⎜⎜⎜⎜⎜⎜⎜⎝⎛Ω−Ψ=ΦBdFSgdVDuddRdyVSfisgrssoso50*50*505050,,,,)1(,,,,,/,/,ξωνωω  Those highly correlated parameters (eqn.2) are selected for later equation development.              (2) (1) 1485Proceedings of ICHE2010, IIT Madras, Aug 2-5,2010 The Sediment Transport Prediction for Mountain Rivers                                                                TOTAL BED MATERIAL LOAD EQUATION Figure 1. Scatter plot between dependent and independent variables 1486Proceedings of ICHE2010, IIT Madras, Aug 2-5,2010 The Sediment Transport Prediction for Mountain Rivers                       The Multiple Linear Regression Analysis (MLR) method was employed to find the relationship between the dependent and independent variables. The transport parameter ( )Φ  by Einstein (1942) was depicted as the dependent variables while the mobility, sediment, conveyance shape, flow resistance and hiding-exposure factor as independent variable. The   regression equation emulated the power concept where the relationship between the dependent variable ( )Φ  and the independent variable ( )X  can be written in the form of equation 7. 1ββ Xo=Φ   Equation 3 can be further expressed in the form of  Xo log  loglog 1ββ +=Φ   The logβo marked as constant and β1 as regression coefficient. If more than one independent is tested, the equation 8 becomes  no XXX log  ......  log  log  loglog n2211 ββββ +++=Φ   A total number of 900 possible combinations was tested and the best combinantion was selected based on four selection criteria.. The first criterion is based on the coefficient of determination or R square values. 2pR  denotes the coefficient of determination for regression model with p-1 independent variables and an intercept term (p) which is less than or equal to the number of variables plus 1. The analysis adds variables to the model up to the point where any additional variable is not useful because it results in a small increase in 2pR . The second criterion is to consider the mean square error MSE (p) for a p variable(s) equation. The model with minimum MSE (p) is chosen. The third criterion is Mallow’s pC statistics. Mallow’s pC is used in multiple linear regression analysis as the criterion for choosing the best subset of predictor effects when a best subset regression analysis is being performed. This measure of the quality of fit for a model tends to be less dependent (than the R square) on the number of effects in the model, and hence it tends to find the best subset that includes only the important predictors of the respective dependent variable. The best model is the one where the Cp value is approximately equal to p (Cp ≈ p). The fourth criterion is based on the modifications of 2pR  that account for the number of variables in the model. While the addition of predictor variables will always cause the coefficient of determination to rise, the adjusted coefficient of determination may fall if the added predictor variables have little explanatory power and are statistically insignificant (Hair et al. 1995). The best regression is as depicted in equation (6) is selected based on the possible combinations of parameters with the value of 2R equal to 0.7, adjusted 2R  equal to 0.678, pC  statistic equal to 4.39 and MSE equal to 0.409. 336.0603.0117.1501110138.2−− Ω×=Φ isvdξω The analysis was further extended by calculating the population means and population standard (3) (4) (5)      (6) 1487Proceedings of ICHE2010, IIT Madras, Aug 2-5,2010 The Sediment Transport Prediction for Mountain Rivers                      deviation of the discrepancy ratio, denoted by σ, to show the variance of values from the mean discrepancy ratio values. The values of discrepancy ratios were then averaged and the functions were recommended based on the mean of the discrepancy ratio (Raphelt, 1990). The closer the value to unity and smaller the standard deviation, the better suited the total sediment load equation is assumed for the current data set. Since the transport parameter,350)1-( dSgVRCsν=Φ , consist of volumetric concentration, Cv, then the equation 6 can be expressed as:  ( )[ ] 21350336.0603.0117.15011 110138.2 dSgVRvdC sisV −×⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎣⎡×Ω×××=−−ξω The newly developed equation (7) was validated with the existing data from Malaysian high gradient rivers. The discrepancy ratio test; which is a ratio of predicted total bed material load over measured total bed material load with an acceptable range of 0.5–2.0, is checked. An approximate 54.5% of the tested data lay within the acceptable range of 0.5-2.0. As comparison, Figure 2 shows the comparison between measured sediment load and calculated sediment load.   Comparison Between Measured and Calculated Total Load0.000010.00010.0010.010.11100.00 0.00 0.00 0.01 0.10 1.00 10.00Measured Sedimentl Load,TjCalculated Sediment Load,Tj Figure 2. Comparison between measured sediment load and calculated sediment load using equation 7      CONCLUSION   (7) 1488Proceedings of ICHE2010, IIT Madras, Aug 2-5,2010 The Sediment Transport Prediction for Mountain Rivers                       The newly developed equation is suitable to be applied for coarser bed river with 50d ranges from 2.00 mm to 147.43 mm and the results are very much influenced by the hiding and exposure effect. It is evident that the smaller grains sheltered within a mixture of sizes tend to be harder to move than the smaller grains in uniform size bed. Thus, to cater closely this phenomenon, the present equation incorporates the hiding exposure function which is obviously affecting the sediment transport prediction at the high gradient rivers. However, the physical characterization of the microform such as particle clusters, emerged or submerged conditions of the bed materials and their effect to the bed material transport and flow structure were not being the main focus of the present study and can be a good subject matter for further analysis. One should take note that the predictive power of the present equation is up to 54.5 %. This equation can be further improved by fitting the high flow data which is not considered during the course of this study.   ACKNOWLEDGEMENT   We would like to thank the Ministry of Science, Technology and Innovation (MOSTI) for funding this research by awarding the National Science Fellowship (NSF) and through E-science grant (04-01-01-SF0126). Many thanks also due to Water Resources Engineering and Management Research Centre (WAREM) and Faculty of Civil Engineering UiTM Penang for their support and encouragement to conduct this research successfully.  REFERENCES  Ackers, P., and White, W.R. 1973. Sediment transport. New approach and  analysis, Journal of the Hydraulic Division. 99 (HY11), 2041-2060. Ariffin, J., Ab. Ghani, A., Zakaria, N.A., Yahya, A.S. and Abdul Talib, S. 2001.  Sediment discharge on Sungai Langat and its tributaries. In Proceedings, R & D  Colloquium on River Engineering and Urban Drainage (Ab. Ghani, A., Zakaria,  N.A., Abdullah, R & Mohd. Sidek, L., ed), 14-15 August. p. 72-78. Penang,  Malaysia. Bagnold, R.A. 1966. An approach to the sediment transport problem from  general physics, U.S Geological Survey Professional Paper No. 422-J. Colby, B.R. & Hambree, C.H. 1955. Computation of total sediment discharge,  Niobrara River near Cody, Nebraska, U.S Geological Survey, Water Supply Paper  1357. Colby, B.R. (1964), Practical computation of bed material discharge. In  Proceedings, ASCE 90 (HY2). DuBoys, M.P. 1879. Le rhone et les rivieres a lit affouillable, Annales de Ponts  et Chausses, sec.5, vol. 18, pp. 141-195.  Egiazaroff, I. V. 1965. Calculation of nonuniform sediment concentrations. J.  Hydraul. Div., 91(HY4), 225–248. Einstein, H.A. 1942. Formula for the transportation of bed load. Transactions of  the ASCE, vol. 107. Einstein, H.A. 1950. The bedload function for sediment transportation in open  channel flows. U.S Department of Agriculture Soil Conservation Service  Technical Bulletin, no. 1026.  1489Proceedings of ICHE2010, IIT Madras, Aug 2-5,2010 The Sediment Transport Prediction for Mountain Rivers                      Einstein, H.A. 1950. The bedload function for sediment transportation in open  channel flows. U.S Department of Agriculture Soil Conservation Service  Technical Bulletin, no. 1026. Engelund, F. & Hansen, E. 1967. A Monograph on Sediment Transport in  Alluvial Streams. Copenhagen: Teknisk Vorlag. Graf, W.H. 1971. Hydraulics of sediment transport, McGraw-Hill, New York. Hair, J.F., Anderson, R.E., Tathan, R.L. & Black, W. 1995. Multivariate data  analysis with reading, 4th Ed., New Jersey: Prentice Hall Inc. Laursen, E.M. 1958. The total sediment load of streams, Journal of the  Hydraulic Division. 84(HY1), 1530-1-1530-36. Meyer-Peter, E., & Muller, R. 1948. Formulas for bed load transport.  International Association of Hydraulic Research, 2nd Meeting Documents, IAHR:  Stockholm. Parker, G. 1990. Surface based bedload transport relationship for gravel rivers,  Journal of Hydraulic Research, vol. 28, no.4, pp. 417-436. Schoklitsch, A. 1934. Der geschiebetrieb und die geschiebefracht, Wasserkraft  und Wasserwirtschaft, vol. 29, no.4, pp. 37-43. Sinnakaudan,S.,A.Ab Ghani, M.S.S. Ahmad., & N.A. Zakaria. 2006. Multiple  linear regression model for total bed material load prediction, J.Hydraul.Eng.,  132(5) 521-528. Thompson, C.J. 2006. The geomorphology of Southeast Australian mountain  streams. Phd thesis: University of New South Wales, unpublished. Toffaleti, F.B. 1969. Definitive computation of sand discharge in rivers, J.  Hydraul. Eng., 95(HY1), 225-246. Yang, C.T. 1972. Incipient motion and sediment transport, Journal of the  Hydraulics Division, 99(HY10), 1679-1704. Yang, C.T. 1984. Unit stream power equations for gravel, J. Hydraul. Eng.,  110(HY12), 1783-1797. Yang, C.T. 1996. Sediment Transport: Theory and Practice. McGraw-Hill, New  York.   1490

The Sediment Transport Prediction For Mountain Rivers

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The Sediment Transport Prediction For Mountain Rivers

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