Session Details | |
Section | HS - Hydrological Sciences |
Session Title | Advances In Ensemble Hydro-meteorological Forecasting: Methods And Applications |
Main Convener | Dr. David Robertson (Commonwealth Scientific and Industrial Research Organisation, Australia) |
Co-convener(s) | Prof. Qingyun Duan (Beijing Normal University, China) Dr. Sunmin Kim (Kyoto University, Japan) Dr. Sean Turner (Singapore University of Technology and Design, Singapore) Dr. Jaepil Cho (APEC Climate Center, Korea, South) |
Session Description | Ensemble hydro-meteorological forecasts provide valuable information enabling users to undertake proactive, risk-based management of water resources systems and planning emergency responses. Applications that can benefit from accurate and reliable forecasts include flood warning, reservoir control, environmental water management, water use planning, hydropower production and drought risk management. This session aims to discuss recent developments in methods for ensemble hydro-meteorological forecasting and the application of forecasts for real-world water resources planning and management. Contributions are invited on rainfall and streamflow forecasting applicable to forecast lead-times extending from hours to 12 months. Specific contributions may include: - Integration of weather and climate forecasts into streamflow forecasting - Methods for efficiently post-processing atmospheric predictions for hydrological applications - Evaluation of ensemble hydro-meteorological forecasts - Methods for estimating and reducing hydrologic predictive uncertainty including multi-model combination, data assimilation and post processing - Communicating predictive uncertainty and developing risk-based decision support systems for emergence management - Application of forecast information for water resources management including system simulation and optimisation The session is organized under the auspices of HEPEX (www.hepex.org), which was launched in 2004 to foster scientific developments necessary to improve the skill of probabilistic hydrological predictions and their use in operational contexts. |