Sensitivity Analysis of Two-Dimensional Pollution Transport Model Parameters in Shallow Water Using RSA Method

Document Type : Research Paper

Authors

1 PhD Student of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran

2 Assistant Professor, Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran

3 Associate Professor, Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran

4 Associate Professor, Department of Civil Engineering, University of Birjand, Birjand, Iran

Abstract

Understanding of the fate of temporal and spatial distribution of pollutionis an essential subject for prediction of damages, caused by pollution,  on the ecology of rivers and coastal areas. It is also necessary to provide efficient solutions for both pollution control and environmental protection. In this study, shallow water equations have been used to simulate pollution transport by 2-dimensional finite volume method. Calibration-modification and frequent changing of the amount of parameters is a known issue in hydraulic and hydrologic models. Therefore, it is necessary to utilize methods for sensitivity analysis and reduction of the parameters number to calibrate models. In this study, the RSA sensitivity analysis method was used for each parameter in which the ratio of sensitivity and cumulative distribution function for the sets of good and bad parameters are computed.. For this purpose, 5000 iterations from uncertainty domain of calibration parameters of pollution transport model in a Standard issue of shallow water were performed by using uncertainty algorithm GLUE. With enforcing the acceptable threshold values for the sumof square error index on the total simulation results, 1000 premier simulations were introduced as an efficient simulation. The corresponded set of parameters was considered as good set parameters and the others as bad set parameters. Thus, the sensitivity index was calculated for the Manning coefficient and the floor slope in the x and y directions. The comparison of sensitivity analysis of parameters based on the RSA methods and variation coefficient of parameters indicated that RSA is an efficient method for sensitivity analysis of model parameters.

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Aliparast, M. (2009). Two-dimensional finite volume method for dam-break flow simulation. International Journal of Sediment Research. 24(1): 99-107.
Benkhaldoun, F. (2007). Well-balanced finite volume schemes for pollutant transport on unstructured meshes, Journal of Computational Physics, 226, 180–203.
Beven, K.J and Binley, A. (1993). The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes. 6(3): 279-298.
Beven, K. J. (2001). Rainfall-Runoff Modeling, Tthe Primer. John Wiley Pub., Chichester, UK.
Blasone, R. Vrugt, J. Madsen, H. Rosbjerg, D. Robinson, B. Zyvoloski, G. (2008). Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling, Advances in Water Resources, 31(4), 630-648.
Butts, M. B., Payne, J. T., Kristensen, M. and Madsen, H. (2004). An evaluation of the impact  of model structure on hydrological modelling uncertainty for streamflow simulation, Journal of Hydrology, 298, 222-241.
Campbell, E.P., Cox, D.R and Bates, B.C. (1990). A bayesian approach to parameter estimation and pooling in nonlinear flood event models. Water Resources Research. 35(1): 83-98.
Feyereisen, G. W., Strickland, T. C. Bosch, D. D. and Sullivan, D. G. (2007). Evaluation of SWAT manual calibration and input parameter sensitivity in the little river watershed, Transactions of the ASABE, 50(3), 843−855.
Heidari, A., Saghafian, B and Maknoon, R. (2005). Flood Hydrograph Simulation with Uncertainty in Rainfall _ Runoff. Journal of Advanced Materials in Engineering (Esteghlal), 23(2):93-111, (In Farsi).
Khorashadizadeh, M., Hashemimonfared, S.A., Akbarpour, A and Pourrezabilondi, M. (2016). Uncertainty Assessment of Pollution Transport Model Using GLUE Method. Iranian Journal of Irrigation and Drainage, 3(10): 284-293, (In Farsi).
Kucherenko, S., Rodriguez-Fernandez, M., Pantelides, C and Shah, N. (2009). Monte Carlo evaluation of derivative-based global sensitivity measures. 5th International Conference on Sensitivity Analysis of Model Output. ELSEVIER SCI LTD, 1135-1148.
Lambert, R., Lemke, F., Kucherenko, SS., Song, S and Shah, N. (2016). Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling. MATHEMATICS AND COMPUTERS IN SIMULATION, 42-54.
Li, S and Duffy,C. (2012). Fully-Coupled Modeling of Shallow Water Flow and pollutant Transport on Unstructured Grids. Procedia Enviromental Sciences. 13, 2098-2121.
Mertens, J., Madsen, H., Kristensen, M., Jacques, D and Feyen1, J. (2005). Sensitivity of soil parameters in unsaturated zone modelling and the relation between effective laboratory and in situ estimates. Hydrological Processes, 19, 1611- 1633.
Paik, J. and Park, S. D. (2011). Numerical simulation of flood and debris flows throughdrainage culvert, Italian Journal of Engineering Geology and Environment, 15, 487-493.
Saltelli, A., Tarantola, S., Campolongo, F and Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, England.
Sobol, IM., Tarantola, S., Gatelli, D., Kucherenko, S and Mauntz W. (2007). Estimating the approximation error when fixing unessential factors in global sensitivity analysisRellability Engineering & System Safety, 957-960.
Vazquez, M. E. (1999). Improved treatment of source terms in upwind schemes for the shallowwater equations in channels with irregular geometry, Journal of Computational Physics, 148, 497–526.
Xiong, L and O’Connor,K.M. (2008). An empirical method to improve the prediction limits of the GLUE methodology in rainfall–runoff modeling. Journal of Hydrology. 349, 115-124.
Zhang, C., Chu, J and Fu G. (2013). Sobol’s sensitivity analysis for a distributed hydrological model   of Yichun River Basin. Journal of Hydrology. 480, 58-68.