Application of modified nonlinear storage function on runoff estimation

Abstract This study proposed a modified nonlinear storage function runoff model to take into account the storage hysteresis effect, in which there exists difference of the storage–discharge relationship between the rising and recession limb. Since the modified storage function runoff has seven parameter, a parameter-calibration method, which combines the genetic algorithm with the least square criterion. For model calibration and validation, twenty rainfall–runoff events (1968–2005) recorded at Wudu gage in Keelung River in northern Taiwan were used in the study. The results of model validation reveal that the modified storage function runoff model not only produces the realistic storage–discharge relationship, but also provides a good estimation of the runoff.

[1]  Pao Shan Yu,et al.  Comparison of uncertainty analysis methods for a distributed rainfall–runoff model , 2001 .

[2]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[3]  K. Lansey,et al.  Evaluation of the storage function model parameter characteristics , 1997 .

[4]  M. Franchini.  Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall-runoff models , 1996 .

[5]  Henrik Madsen,et al.  Comparison of different automated strategies for calibration of rainfall-runoff models , 2002 .

[6]  Kazuaki Hiramatsu,et al.  Vagueness ratio of storage function model parameters and its relation to the accuracy of hydrograph prediction , 2003, Paddy and Water Environment.

[7]  VERIFICATION AND APPLICATION OF REGIONAL EQUATIONS FOR THE STORAGE FUNCTION RUNOFF MODEL 1 , 1999 .

[8]  Qi Zhang,et al.  Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model , 2010 .

[9]  R. Prasad.  A Nonlinear Hydrologic System Response Model , 1967 .

[10]  Vijay P. Singh,et al.  Comparison of simple runoff models used in Korea for small watersheds , 1999 .

[11]  Q. J. Wang.  The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models , 1991 .

[12]  Henrik Madsen,et al.  Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. , 2000 .

[13]  Zbigniew W. Kundzewicz,et al.  Nonlinear models of dynamic hydrology , 1986 .

[14]  I. Chaubeya,et al.  Uncertainty in the model parameters due to spatial variability of rainfall , 1999 .