| Project/Area Number |
20K22428
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund |
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| 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 |
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Principal Investigator |
ZHOU XUDONG 東京大学, 生産技術研究所, 特任助教 (20876239)
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| Project Period (FY) |
2020-09-11 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | Water surface area / Hydrodynamic model / Data assimilation / Model development / Assessment / Assimilation / Water surface elevation / bias correction / assessment system / levee scheme / general agreement / explainable mismatches / water surface area / CaMa-Flood / data assimilation / satellite |
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| Outline of Research at the Start |
This study proposes to assimilate satellite inundation observations (e.g. Landsat) to improve the accuracy of global inundation estimates by a hydrodynamic model (i.e. CaMa-Flood). It will provide fundamental dataset of water surface to relevant research fields in climate and environmental studies.
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| Outline of Final Research Achievements |
Analyzed the global distribution pattern of remote sensing water surfaces and the advantages and disadvantages by comparing it with models. Results show that in areas with high vegetation coverage, remote sensing data underestimates the water surface area, while in areas with intense human activities, the model overestimates the simulation of water surfaces. Developed an evaluation system for hydrodynamic simulation results based on remote sensing data. By using runoff, remote sensing water surface elevation, and remote sensing water surface area, the system achieves automated evaluation of CaMa-Flood simulation results through comprehensive evaluation metrics. The system can also be extended to compare with results from other models. Preliminarily developed a model assimilation method using remote sensing data. By assimilating remote sensing water surface data in different ways, the simulation accuracy of hydrodynamic models has been improved.
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| Academic Significance and Societal Importance of the Research Achievements |
Deepening understanding of remote sensing water surfaces and providing new insights into improving models using sensed data. Relevant research is of great significance for improving flood forecasting and defense capabilities, reducing disaster losses, and improving flood prevention measures.
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