2022 Fiscal Year Annual Research Report
Improving flood and drought prediction using downscaled GRACE terrestrial water storage
| Project/Area Number |
21K20443
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| Research Institution | The University of Tokyo |
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Principal Investigator |
尹 高虹 東京大学, 生産技術研究所, 特任研究員 (00906282)
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| Project Period (FY) |
2021-08-30 – 2023-03-31
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| Keywords | GRACE / TWSA / Downscaling / Deep Learning / LSTM / Flood / Drought |
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| Outline of Annual Research Achievements |
The purpose of the study is to downscale terrestrial water storage anomaly (TWSA) from GRACE satellite in space in order to better capture the spatiotemporal variability of water and its application for flood and drought monitoring and prediction. By the end of the project, I have finished following tasks: (1) GRACE TWSA in a region with frequent flood and drought was examined; (2) A synthetic experiment was conducted to validate the assumption of TWSA downscaling; (3) A real-world experiment was conducted to downscale GRACE TWSA using Long Short-term Memory (LSTM) model; (4) The capability of downscaled TWSA to monitor and predict floods and droughts at sub-watershed to local scale was validated.
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