A Study on Regionalization of Parameters for Sacramento Continuous Rainfall-Runoff Model Using Watershed Characteristics

- Journal title : Journal of Korea Water Resources Association
- Volume 48, Issue 10, 2015, pp.793-806
- Publisher : Korea Water Resources Association
- DOI : 10.3741/JKWRA.2015.48.10.793

Title & Authors

A Study on Regionalization of Parameters for Sacramento Continuous Rainfall-Runoff Model Using Watershed Characteristics

Kim, Tae-Jeong; Jeong, Ga-In; Kim, Ki-Young; Kwon, Hyun-Han;

Kim, Tae-Jeong; Jeong, Ga-In; Kim, Ki-Young; Kwon, Hyun-Han;

Abstract

The simulation of natural streamflow at ungauged basins is one of the fundamental challenges in hydrology community. The key to runoff simulation in ungauged basins is generally involved with a reliable parameter estimation in a rainfall-runoff model. However, the parameter estimation of the rainfall-runoff model is a complex issue due to an insufficient hydrologic data. This study aims to regionalize the parameters of a continuous rainfall-runoff model in conjunction with a Bayesian statistical technique to consider uncertainty more precisely associated with the parameters. First, this study employed Bayesian Markov Chain Monte Carlo scheme for the estimation of the Sacramento rainfall-runoff model. The Sacramento model is calibrated against observed daily runoff data, and finally, the posterior density function of the parameters is derived. Second, we applied a multiple linear regression model to the set of the parameters with watershed characteristics, to obtain a functional relationship between pairs of variables. The proposed model was also validated with gauged watersheds in accordance with the efficiency criteria such as the Nash-Sutcliffe efficiency, index of agreement and the coefficient of correlation.

Keywords

ungauge;parameter;regionalization;sacramento;multiple linear regression;

Language

Korean

Cited by

References

1.

Beven, K. (2001). Rainfall-RunoffModelling The Primer. John Wiley & Sons Ltd, Chichester, England.

2.

Box, G.E., and Tiao, G.C. (1973). Bayesian inference in statistical analysis. Addison-Wesely publishing company.

3.

Burnash, R.J.C., Ferral, R.L., and McGuire, R.A. (1973). A generalized streamflow simulation system, conceptual modeling for digital computers. Joint Federal, State River Forecast Center, Sacramento, CA.

4.

Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2004). Bayesian Data Analysis. Chapman & Hall/ CRC.

5.

Gelman, A., and Rubin, D.B. (1992). "Inference from iterative simulations using multiple sequences (with discussion)." Statistical Science, Vol. 7, No. 4, pp. 457-472.

6.

Hasting, W.K. (1970). "Monte carlo sampling methods using markov chains and their applications." Biometrika, Vol. 57, No. 1, pp. 97-109.

7.

Jung, Y.H., Jung, C.G., Jung, S.W., Park, J.Y., and Kim, S.J. (2012). "Estimation of upstream ungaguged watershed streamflow using downstream discharge data." Journal of the Korean Society of Agricultural Engineers, Vol. 54, No. 6, pp. 169-176.

8.

Kim, J.G., Kwon, H.H., and Kim, D.K. (2014). "A development of hourly rainfall simulation Technique Based on Bayesian MBLRP Model." Journal of the Korean Society of Civil Engineers, Vol. 34, No. 3, pp. 821-831.

9.

Kim, S.U., and Lee, K.S. (2008). "At-site low flow frequency analysis using bayesian MCMC: I. theoretical background and construction of prior distribution." Journal of Korea Water Resources Association, Vol. 41, No. 1, pp. 35-47.

10.

Kim, U., and Kaluarachchi, J.J. (2008). "Application of parameter estimation and regionalization methodologies to ungauged basins of the upper blue Nile river basin, Ethiopia." Journal of Hydrology, Vol. 362, No. 1, pp. 39-56.

11.

Krause, P., Boyle, D.P., and Base, F. (2005). "Comparison of different efficiency criteria for hydrological model assessment." Advances in Geosciences, Vol. 5, pp. 89-97.

12.

Kwon, H.-H., Moon, Y.-I., Kim, B.-S., and Yoon, S.-Y. (2008). "Parameter optimization and uncertainty analysis of the NWS-PC rainfall-runoff model coupled with bayesian markov chain monte carlo inference scheme." Journal of the Korean Society of Civil Engineers, Vol. 28, No. 4B, pp. 383-392.

13.

Kwon, H.-H., Kim, J.-G., Lee, J.-S., and Na, B.-K. (2012). "Uncertainty assessment of single event rainfallrunoff model using bayesian model." Journal of Korea Water Resources Association, Vol. 45, No. 5, pp. 505-516.

14.

Kwon, H.-H., Kim, J.-G., and Park, S.-H. (2013). "Derivation of flood curve with uncertainty of rainfall and rainfall-runoff model" Journal of Korea Water Resources Association, Vol. 46, No. 1, pp. 59-71.

15.

Lee, S.H., and Kang, S.U. (2007). "A parameter regionalization study of a modified tank model using characteristic factors of watersheds." Journal of Korean Society of Civil Engineers, Vol. 27, No. 4-B, pp. 379-385.

16.

Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., and Teller, E. (1953). "Equation of state calculations by fast computing machines." The Journal of Chemical Physics, Vol. 21, No. 6, pp. 1087-1092.

17.

Mwakalila, S. (2003). "Estimation of stream flows of ungauged catchment for river basin management." Physics and Chemistry of the Earth, Vol. 28, pp. 935-942.

18.

Nash, J., and Sutcliffe, J.V. (1970). "River flow forecasting through conceptual models part I-A discussion of principles." Journal of Hydrology, Vol. 10, No. 3, pp. 282-290.

19.

Park, Y.H., and Yoo, C.S. (2008). "Evaluation of stream flow data observed in the pyungchang river basin using the IHACRES model." Journal of The Korean Society of Hazard Mitigation, Vol. 8, No. 4, pp. 123-133.

20.

Scargle, J.D. (1998). "Studies in astronomical time series analysis. V. Bayesian blocks, a new method to analyze structure in photon counting data." The Astrophysical Journal, Vol. 504, No. 1, pp. 405.

21.

Sorooshian, S., Duan, Q., and Gupta, V.K. (1993). "Calibration of rainfall-runoff models: Application of global optimization to the sacramento soil moisture accounting model." Water Resources Research, Vol. 29, No. 4, pp. 1185-1194.

22.

Walpole, R.E., Myers, R.H., Myers, S.L., and Ye, K. (2002). Probability and Statistics for Engineers and Scientists, Upper Saddle River, NJ: Prentice-Hall

23.

Willmott, C.J. (1981). "On the validation of models." Physical Geography, Vol. 2, No. 2, pp. 184-194.