Improved Hydrologic Model Calibration based on Coupled Monte Calro and Bayesian Methods

Document Type : Original Article

Authors

1 Ph.D. in Civil Engineering, Water and Power Resources Development Coorporation of Iran

2 Associate Professor of Soil Conservation and Watershed Management Research Institute

3 Assistant Professor, School of Civil and Environmental Engineering Amirkabir University of Technology

Abstract

In this paper, uncertainty of a rainfall – runoff (RR) model is analyzed based on combination of Monte Carlo (MC) procedure and Bayesian theory, which is known as GLUE framework. The rainfall–runoff transformation was performed by ModClark distributed – conceptual model. In this model, the basin’s hydrograph is determined by the superposition of runoff generated by individual cells in a raster – based discretization. Application of MC in uncertainty analysis introduces convenient parameter variation range, which is not adjustable based on new data. In GLUE method, however, Bayesian theory is applied to update prediction limits and distribution of parameter as new data becomes available. Goodness of fit criteria is selected such that higher discharges of hydrograph are given larger weights compared to other parts of the hydrograph. Uncertainty of RR model parameters was assessed in Gharasoo basin, a subbasin of the great Karkheh river basin. The results show that GLUE has a good performance in updating model parameters in comparison with MC method alone.
 

Keywords

References

حیدری، علی، بهرام، ثقفیان و رضا، مکنون، (1383)، شبیه‌سازی آبنمود سیل با در نظر گرفتن عدم قطعیت پارامترهای مدل‌های بارش – رواناب، نشریه مهندسی استقلال.
Bates, B. C. and Campbell, E. P. (2001), “A Morkov Chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall – runoff modeling,” Water Resource Research, 37(4), pp. 937-947.
Beven, K. J. (2000), Rainfall – Runoff modeling, John Wiley & Sons, LTD, pp. 314.
Beven, K. J. (2001), “How can we go in distributed hydrological modeling?,” Hydrology & Earth System Sciences ,5(1), pp. 1-12.
Beven, K. J (2002), “Uncertainty and the detection of structural change in models of environmental system,” In: Manifesto A , Beck MB (eds) Environmental foresight and models: Chapter 12. pp. 227-250.
Beven, K. J. and Binley, A. (1992), “The future of distributed models: model calibration and uncertainty prediction,” Hydrological Processes, 6(3), pp. 279-298.
 
 
Beven, K. J. and  Freer, J.(2001), “Equifinality, data assimilation and uncertainty estimation in mechanistic modeling of complex environmental systems using the GLUE methodology,” Journal of Hydrology , 249, pp.11-29.
Campbell, E.P., Fox, D.R. and Bates, B. C. (1999), “A Bayesian approach to parameter estimation and pooling in nonlinear flood event models,” Water Resources Research, 35(1), pp. 211-220.
Feyen, L., Beven, K. J., De Smedt, F and Freer, J. (2001), “Stochastic capture zone delineation within the generalized likelihood uncertainty estimation methodology: Conditioning on head observations,” Water Resources Research, 37(3), pp.101-120.
Freer, J. and Beven, K. J. (1996), “Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the GLUE approach,” Water Resource Research, 32(7), pp. 2161-2173.
Franks, S. and Beven, K. J. (1997), “Bayesian estimation of uncertainty in land surface-atmosphere flux predictions,” Journal of Geophysical Research-Atmospheres.
Georgakakos, K. P. (1986), “A Generalized Stochastic Hydro meteorological Model for Flood and Flash-Flood Forecasting, 1-Formulation,” Water Resources Research, 22(13), pp. 2085-2095.
Georgrakakos, K. P. and Bras, R. L. (1982), A Precipitation Model and its use in Real Time River Flow Forecasting, Ralph M. Parsons Laboratory Hydrology and Water Resource System, 301 P.
Hankin, B. G., Hardy, R., Kettle, H. and Beven, K. J. (2001), “Using CFD in a GLUE framework to model the flow and dispersion characteristics of a natural fluvial dead zone,” Earth Surface Processes and Landforms, 26(6), pp. 667-687.
Hornberger, G. M. and Spear, R. C. (1981), “An approach to the preliminary analysis of environmental systems,” Journal of Environmental Management, 12, pp. 7-18.
Kitanidis, P. K. (1986), “Parameter uncertainty in estimation of spatial function: Bayesian Analysis,” Water Resources Research, 22(4), pp. 499-507.
Kuczara, G. and Parent, E. (1998), “Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm,”, Journal of Hydrology, 211, pp. 69-85.
Kull, D. and Feldman, A. (1998), “Evolution of Clark’s unit graph method to spatially distributed runoff,” Journal of Hydrologic Engineering, ASCE , 3(1), pp. 9-19.
Peters, J and Easton, D. (1996), “Runoff simulation using radar rainfall data,” Water Resource Bulletin , AWRA, 32(4), pp. 753-760.
Yapo, P., Gupta, H. and Sorooshian, S. (1998), “Multi – objective global optimization for hydrological models,” Journal of Hydrology 
Iran-Water Resources Research
Volume 1, Issue 3
October 2005
Pages 29-40

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History

  • Receive Date: 19 November 2003
  • Accept Date: 13 November 2005

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