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Fang, Hong Wei; Lai, Rui Xun; Lin, Binliang; Xu, Xing Ya; Zhang, Fang Xiu; Zhang, Yue Feng (2016)
Publisher: American Society of Civil Engineers
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics, Physics::Geophysics
The heavy sediment load of the Yellow River makes it difficult to simulate sediment concentration using classic numerical models. In this paper, on the basis of the classic one-dimensional numerical model of open channel flow, a variational-based data assimilation method is introduced to improve the simulation accuracy of sediment concentration and to estimate parameters in sediment carrying capacity. In this method, a cost function is introduced first to determine the difference between the sediment concentration distributions and available field observations. A one-dimensional suspended sediment transport equation, assumed as a constraint, is integrated into the cost function. An adjoint equation of the data assimilation system is used to solve the minimum problem of the cost function. Field data observed from the Yellow River in 2013 are used to test the proposed method. When running the numerical model with the data assimilation method, errors between the calculations and the observations are analyzed. Results show that (1) the data assimilation system can improve the prediction accuracy of suspended sediment concentration; (2) the variational inverse data assimilation is an effective way to estimate the model parameters, which are poorly known in previous research; and (3) although the available observations are limited to two cross sections located in the central portion of the study reach, the variational-based data assimilation system has a positive effect on the simulated results in the portion of the model domain in which no observations are available.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Abbott, M. B., and Ionescu, F. (1967). “On the numerical computation of nearly horizontal flows.” J. Hydraul. Res., 5(2), 97-117.
    • Ackers, P., and White, W. R. (1973). “Sediment transport: New approach and analysis.” J. Hydraul. Div., 99(11), 2041-2060.
    • 04016010-10 Bélanger, E., and Vincent, A. (2005). “Data assimilation (4D-VAR) to forecast flood in shallow-waters with sediment erosion.” J. Hydrol., 300(1-4), 114-125.
    • Cao, Z., Pender, G., Wallis, S., and Carling, P. (2004). “Computational dam-break hydraulics over erodible sediment bed.” J. Hydraul. Eng., 10.1061/(ASCE)0733-9429(2004)130:7(689), 689-703.
    • Celik, I., and Rodi, W. (1988). “Modeling suspended sediment transport in nonequilibrium situations.” J. Hydraul. Eng., 10.1061/(ASCE)0733 -9429(1988)114:10(1157), 1157-1191.
    • Chanson, H. (2004). Environmental hydraulics of open channel flows, Elsevier Butterworth Heinemann, Amsterdam, Netherlands.
    • Chen, W., and Chau, K. W. (2006). “Intelligent manipulation and calibration of parameters for hydrological models.” Int. J. Environ. Pollut., 28(3-4), 432-447.
    • Cheng, C., Chau, K., Sun, Y., and Lin, J. (2005). “Long-term prediction of discharges in manwan reservoir using artificial neural network models.” Advances in Neural Networks-ISNN 2005, J. Wang, X.-F. Liao, and Z. Yi, eds., Springer, Berlin, 1040-1045.
    • Ch'ien, N., and Wan, C. (1999). Mechanics of sediment transport, ASCE, Reston, VA.
    • Cunge, J. A. (1980). Practical aspects of computational river hydraulics, Pitman Advanced Publications Program, Boston.
    • Engelund, F., and Hansen, E. (1967). A monograph on sediment transport in alluvial streams, Teknish Forlag Technical Press, Copenhagen, Denmark.
    • Errico, R. M. (1997). “What is an adjoint model?” Bull. Am. Meteorol., 78(11), 2577-2591.
    • Evensen, G. (2009). Data assimilation: The ensemble Kalman filter, Springer, Dordrecht, Holland.
    • Fang, H., Chen, M., and Chen, Q. (2008). “One-dimensional numerical simulation of non-uniform sediment transport under unsteady flows.” Int. J. Sediment Res., 23(4), 316-328.
    • Fang, H. W., and Wang, G. Q. (2000). “Three-dimensional mathematical model of suspended-sediment transport.” J. Hydraul. Eng., 10.1061/ (ASCE)0733-9429(2000)126:8(578), 578-592.
    • Haimann, M., Liedermann, M., Lalk, P., and Habersack, H. (2014). “An integrated suspended sediment transport monitoring and analysis concept.” Int. J. Sediment Res., 29(2), 135-148.
    • Han, Q. W. (1980). A study on the non-equilibrium transportation of suspended load, Guanghua, Beijing, 793-802.
    • Hu, P., Cao, Z., Pender, G., and Liu, H. (2014). “Numerical modelling of riverbed grain size stratigraphic evolution.” Int. J. Sediment Res., 29(3), 329-343.
    • Kalnay, E. (2003). Atmospheric modeling, data assimilation, and predictability, Cambridge University Press, New York.
    • Lai, R., Fang, H., He, G., Yu, X., Yang, M., and Wang, M. (2013). “Dual state-parameter optimal estimation of one-dimensional open channel model using ensemble Kalman filter.” J. Hydrodyn., Ser. B, 25(4), 564-571.
    • Lai, R. X., Fang, H. W., and Xu, X. Y. (2014). “Dynamic numerical model for the prediction of water and sediment transport.” Chin. J. Hydraul. Eng., 45(8), 930-937 (in Chinese).
    • Le Dimet, F. X., and Talagrand, O. (1986). “Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects.” Tellus A, 38A(2), 97-110.
    • Legates, D. R., and McCabe, G. J. (1999). “Evaluating the use of 'goodness-of-fit' measures in hydrologic and hydroclimatic model validation.” Water Resour. Res., 35(1), 233-241.
    • Lewis, J. M., Lakshmivarahan, S., and Dhall, S. K. (2006). “Dynamic data assimilation: A least squares approach.” Encyclopedia of mathematics and its applications, Cambridge University Press, Cambridge, U.K.
    • Liggett, J. A., and Cunge, J. A. (1975). “Numerical methods of solution of the unsteady flow equations.” Unsteady flow in open channels, Water Resources Publications, CO, 484.
    • Lu, X. X., Ran, L. S., Liu, S., Jiang, T., Zhang, S. R., and Wang, J. J. (2013). “Sediment loads response to climate change: A preliminary study of eight large Chinese rivers.” Int. J. Sediment Res., 28(1), 1-14.
    • Ministry of Water Resources of the PRC. (1992). “Specification for the measurement of suspended sediment load.” GB 50159-92, China (in Chinese).
    • Moradkhani, H., Sorooshian, S., Gupta, H. V., and Houser, P. R. (2005). “Dual state-parameter estimation of hydrological models using ensemble Kalman filter.” Adv. Water Resour., 28(2), 135-147.
    • Navon, I. M. (1986). “A review of variational and optimization methods in meteorology.” Variational methods in geosciences, developments in geomathematics, Elsevier, Amsterdam, Netherlands, 29-34.
    • Preissmann, A. (1961). “Propagation des intumescences dans les canaux et Les Rivieres.” 1 Congres de l'Association Francaise de Calcule, Grenoble, France.
    • Samaras, A. G., and Koutitas, C. G. (2014). “Modeling the impact of climate change on sediment transport and morphology in coupled watershed-coast systems: A case study using an integrated approach.” Int. J. Sediment Res., 29(3), 304-315.
    • Sanders, B. F., and Katopodes, N. D. (2000). “Adjoint sensitivity analysis for shallow-water wave control.” J. Eng. Mech., 10.1061/(ASCE)0733 -9399(2000)126:9(909), 909-919.
    • Schlitzer, R. (1993). “Determining the mean, large-scale circulation of the atlantic with the adjoint method.” J. Phys. Oceanogr., 23(9), 1935-1952.
    • Stroud, J. R., Lesht, B. M., Schwab, D. J., Beletsky, D., and Stein, M. L. (2009). “Assimilation of satellite images into a sediment transport model of Lake Michigan: Satellite data assimilation in lake Michigan.” Water Resour. Res., 45(2), W02419.
    • Talagrand, O. (2010). “Variational assimilation.” Data assimilation, W. Lahoz, B. Khattatov, and R. Menard, eds., Springer, Berlin, 41-67.
    • Taormina, R., and Chau, K. (2015). “Neural network river forecasting with multi-objective fully informed particle swarm optimization.” J. Hydroinf., 17(1), 99-113.
    • Thornhill, G. D., Mason, D. C., Dance, S. L., Lawless, A. S., Nichols, N. K., and Forbes, H. R. (2012). “Integration of a 3D variational data assimilation scheme with a coastal area morphodynamic model of Morecambe Bay.” Coastal Eng., 69(11), 82-96.
    • Tsai, C. W., Man, C., and Oh, J. (2014). “Stochastic particle based models for suspended particle movement in surface flows.” Int. J. Sediment Res., 29(2), 195-207.
    • van Rijn, L. C. (1986). “Mathematical modeling of suspended sediment in nonuniform flows.” J. Hydraul. Eng., 10.1061/(ASCE)0733-9429 (1986)112:6(433), 433-455.
    • van Rijn, L. C. (2007a). “Unified view of sediment transport by currents and waves. I: Initiation of motion, bed roughness, and bed-load transport.” J. Hydraul. Eng., 10.1061/(ASCE)0733-9429(2007)133:6(649), 649-667.
    • van Rijn, L. C. (2007b). “Unified view of sediment transport by currents and waves. II: Suspended transport.” J. Hydraul. Eng., 10.1061/(ASCE) 0733-9429(2007)133:6(668), 668-689.
    • Wang, G., Wu, B., and Wang, Z.-Y. (2005). “Sedimentation problems and management strategies of Sanmenxia reservoir, Yellow River, China: Management strategies of Sanmenxia reservoir.” Water Resour. Res., 41(9), W09417.
    • Wu, B., van Maren, D. S., and Li, L. (2008a). “Predictability of sediment transport in the Yellow River using selected transport formulas.” Int. J. Sediment Res., 23(4), 283-298.
    • Wu, B., Wang, G., Xia, J., Fu, X., and Zhang, Y. (2008b). “Response of bankfull discharge to discharge and sediment load in the Lower Yellow River.” Geomorphology, 100(3-4), 366-376.
    • Wu, C. L., Chau, K. W., and Li, Y. S. (2009). “Methods to improve neural network performance in daily flows prediction.” J. Hydrol., 372(1-4), 80-93.
    • Yang, C. T. (1996). Sediment transport: Theory and practice, McGraw-Hill, New York.
    • Yellow River Conservancy. (2013). “The Yellow River sediment bulletin.” Zhengzhou, China (in Chinese).
    • Yu, L., and O'Brien, J. J. (1991). “Variational estimation of the wind stress drag coefficient and the oceanic Eddy viscosity profile.” J. Phys. Oceanogr., 21(5), 709-719.
    • Zhang, M., and Zhang, F. Q. (2012). “E4DVar: Coupling an ensemble Kalman filter with four-dimensional variational data assimilation in a limited-area weather prediction model.” Mon. Weather Rev., 140(2), 587-600.
    • Zhang, R. J. (1961). River dynamics, China Industry Press, Beijing (in Chinese).
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