| 研究実績の概要 |
Flood forecasting and the related damage assessment in real-time are challenging and currently not available in the Lower Mekong region. This study focuses on the development of a real-time system of flood inundation in the Lower Mekong Basin (LMB). In particular, this study focuses on the developing a system to assess flood hazards and damages in the LMB simultaneously. The developed system can also be used for risk assessment of agricultural damages for future projections of extreme flood events under climate change effects.
Firstly, we have collected and evaluated the observed hydrological data. Flood inundation simulation was conducted in the LMB under the effect of climate change using a large ensemble climate data (d4PDF), MRI-AGCM3.2, and CMIP6 GCM datasets. Flood hazards and its related damages were assessed. Moreover, flood forecasting was primarily evaluated using numerical weather prediction dataset from GSMaPxNEXRA through NICAM-LETKF data assimilation in the Prek Thnot River Basin, Cambodia. Finally, the evaluation and improvement of flood forecasting has been done to improve its performance and accuracy by combining with machine learning technique. Hybrid approach of hydrological model.
|
