Hydrological modeling approach using radar-rainfall ensemble and multi-runoff-model blending technique

Abstract

The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoffsimulations. We propose a blending technique using a rainfall ensemble and runoffsimulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoffmodels that use rainfall ensembles as input data to perform a runoffanalysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808-3.354 dBR. We derived a runoffhydrograph with greatly reduced uncertainty by applying the blending technique to the runoffsimulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoffmodels.