A synthetic equation for storage function model parameter estimation based on kinematic wave approximation

초록

The storage function model (SFM) is a rainfall-runoff model widely used in Korea for flood forecasting and warning systems. The traditional equations for parameter estimation are based on observed data under limited conditions, and so it is difficult to sensitively consider the factors affecting actual runoff. The aim of this study is to develop synthetic equations for SFM parameter estimation that are useful for ungauged watersheds, through 35 000 applications of the distributed kinematic wave model (KWM) for virtual watersheds. The parameter estimation equation developed in this study was applied to 101 observed events of 16 basins in Korea. We obtained satisfactory results from the application of a synthetic equation to actual basins. That is to say, the peak flood volume simulated by the equation was comparable with the observed volume. The relative error (RE), mean absolute percent error (MAPE), and percent bias (PBIAS) were used as criteria to evaluate the performance of the equation. For the results, RE and MAPE were within 10%, and PBIAS was within 15%.

키워드