2022 Fiscal Year Final Research Report
Improving flood and drought prediction using downscaled GRACE terrestrial water storage
Project/Area Number |
21K20443
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Research Category |
Grant-in-Aid for Research Activity Start-up
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Allocation Type | Multi-year Fund |
Review Section |
0303:Civil engineering, social systems engineering, safety engineering, disaster prevention engineering, and related fields
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Research Institution | The University of Tokyo |
Principal Investigator |
Yin Gaohong 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 / TWS / Downscaling / Deep Learning / Flood / Drought |
Outline of Final Research Achievements |
Flood and drought are global issue causing devastating damage to the ecosystem, human lives, and economics. Monitoring the sptaio-temporal variation of terrestrial water storage (TWS) is important for water management and hazard mitigation. However, current remote sensing-based TWS data has coarse spatial resolution (300 km), which limits its application to sub-regional scale. The study used a deep learning approach to downscale remote sensing-based TWS in space, providing more details of water mass variation at sub-regional to local scale. The downscaled TWS provides a great opportunity to monitor drought and predict flood with high spatial resolution on a sub-regional to local scales. The outcomes of the study make it possible to extent the application of remote sensing-based TWS to regions such as Japan and South Korea.
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Free Research Field |
Hydrology
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Academic Significance and Societal Importance of the Research Achievements |
1. Improve the understanding of the spatiotemporal variation of terrestrial water storage, which is important for water management and related policy making. 2. Monitor and forecast flood and drought on a sub-regional to local scale, which benefits more accurate and targeted hazards mitigation.
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