Streamflow prediction and drought index production based on the Bayesian autoregressive exogenous stochastic volatility model using climate factor

The Soyang Dam is a main multi-purpose dam for preventing floods and supplying water to the metropolitan area, including Seoul, located in the Han River basin. This research explored the predictability of streamflow that plays a critical role in the reservoir operation of the Soyang Dam in South Korea. A novel stochastic approach was used to offer skillful season-ahead streamflow forecasting during the monsoon season (June-July-August, JJA) using climate state variables (e.g., SST, SLP, and wind anomalies) and dynamic climate forecasts simulated from global climate models (GCMs), as predictors. Further, we employes autoregressive exogenous stochastic volatility(ARXSV) model for streamflow prediction with the predictors that were identified within a Hierarchical Bayesian modeling framework. A cross-validation experiment under different synoptic patterns is performed to test the efficacy of the proposed modeling process. Finally, this study will investigate the effectiveness of streamflow forecasts as a precursor of the hydrological drought condition for the upcoming season.

 

Acknowledgement

This research was supported by a grant(2022-MOIS63-001) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety(MOIS, Korea). This work was partially funded by the Korea Meteorological Administration Research and Development Program under Grant KMI 2018-07010.