2022 Fiscal Year Annual Research Report
Global inundation area estimation by assimilating multi-sensor satellite observations into a hydrodynamic model
| Project/Area Number |
20K22428
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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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| Keywords | Assessment / Assimilation / Water surface elevation / Water surface area |
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| Outline of Annual Research Achievements |
The CaMa-Flood model assessment system has undergone improvements and is now being used to evaluate model developments. The first implementation was to select better runoff inputs, which involved testing three different runoff inputs and comparing their corresponding model outputs with observations from in-situ river gauges and satellites. The results showed that the bias-corrected runoff from the VIC model outperformed the other two datasets. The second implementation was to test the bifurcation and kinematic wave configuration in the model. The results showed that bifurcation and dynamic wave movement are important, but sometimes, due to human activities, the model cannot represent the true river topography, leading to deteriorated simulations. The evaluation system has been installed on the lab server, and anyone can access and use it to evaluate model changes. The system was introduced to audiences at the American Geophysical Union conference.
The second achievement was to assimilate satellite observations into the model. Three different ways of assimilating water surface elevation were tested to improve river discharge simulation. The results showed that due to the large systematic bias in the remote sensed observations, assimilation with naturalized observation had better model performance than using original observations. Therefore, before applying sensed observations to real assimilation, adequate pre-processing of the data should be ensured.
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