Future Flow duration Projection of Bayesian Ensemble Model using the IHACRES model

Sukhoon Hyun

Tuesday 30 june 2015

17:48 - 17:51h at North America (level 0)

Themes: (T) Extreme events, natural variability and climate change, (ST) Hydrological extremes: floods and droughts, Poster pitches

Parallel session: Poster pitches: 7I. Extreme - Flood Drought

As the most important information for the climate change assessment in the future, IPCC fifth Assessment Report provide a variety of models of RCP scenario. However, the number of data is much to selecting a model suitable for the current Republic of Korea and the uncertainty is also large, Multi-Model Ensembles are often used. In this study assesses the performance of multi-model ensemble using Bayesian Model Averaging method. Bayesian model averaging method can combine the projection of many models to generate a new one which is expected to be better than individual model projection. BMA is a statistical method that infers the posterior distribution of projecting the variable by weighing individual posterior distributions based on their probabilistic likelihood, with the better performing predictions receiving higher weights than the worse projections. The standard period for selected meteorological observatory total of three Soyangdam basin of the Republic Korea, to perform the verification of the observation period, subject basin was set in 1981~2000 years. The rainfall-runoff model that was used for the analysis of flow duration, was used IHACRES model by using the data of temperature and precipitation in the basin, to calculate the runoff and effective rainfall. Verification results, R2 of the simulation value and the observation was derived good results in 0.92. Therefore, to simulate the inflow using rainfall data of future climate change scenarios applied to BMA technique. Based on the simulation results, the flow duration changes in the future period (2011~2100 years) and to analyze the simulation.