2020 Fiscal Year Research-status Report
Data-driven Seasonal Hydrologic Prediction Using Earth Observing Satellites
| Project/Area Number |
18KK0117
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| Research Institution | The University of Tokyo |
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Principal Investigator |
金 炯俊 東京大学, 生産技術研究所, 特任准教授 (70635218)
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| Co-Investigator(Kenkyū-buntansha) |
渡部 哲史 東京大学, 大学院工学系研究科(工学部), 特任講師 (20633845)
内海 信幸 東京大学, 生産技術研究所, 協力研究員 (60594752)
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| Project Period (FY) |
2018-10-09 – 2023-03-31
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| Keywords | データ駆動型モデリング / 水文季節予報 / 衛星観測 / テレコネクション / 人工知能 |
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| Outline of Annual Research Achievements |
Because of COVID-19, we could not make international trip to exchange among partners. However, remote meeting solutions have been utilized to have several virtual meetings. We could extend this project to connect to a broader community. As results, we could published multiple scientific articles to major journals including Science and Nature Geoscience. Padron et al. (2020) developed a data-driven land surface model and used it to detect human impact on the long-term global water storage changes. Dataset co-developed from this project contributed to the PROFOUND database (Reyer et al., 2020). Kim (2020) estimated the long-term variability of global river discharge and investigated how climate modes (e.g., ENSO) modulates it. Zhang et al., (2020) applied tree-ring based climate reconstruction to detect a tipping-point in hydroclimate at inner East Asia.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
Since virtual meeting became usual exchange format, we could extend a group of partners efficiently. Climate modeling community are connected additionally, and it results in producing multiple high-profile articles. A data-driven land surface model was used to detect human impact on the long-term global terrestrial water storage (TWS) changes represented by multiple climate models. Global freshwater discharge was simulated, and it was found that climate modes such as El-Nino is capable of predicting it several months ahead. Also, we tested a potential use of proxy data such as tree-ring samplings to estimate a longer-term trend which can be a basis of seasonal prediction.
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| Strategy for Future Research Activity |
We will extend the target lakes to include entire 1.4 million lakes listed up in the HydroLAKES data set. To leverage Google Earth Engine platform, an algorithm will be developed using GEE API. It will estimate monthly variability of lake surface extent based on Pekel's global surface water data and lake shape vector from the HydroLAKES data. To covert water surface extent to its volume, area-height relationship will be determined globally combining satellite altimetry measurement. Lake freshwater volume change will be compared against total terrestrial water storage (TWS) variations measured by Gravity Recovery And Climate Experiment (GRACE) satellites. An AI-ba
sed algorithm will be developed to generate a set of surface meteorological variables. It will be extensively used in the next fiscal year for the analysis of surface water balance for each lake basin and determine causal factor on the long-term and seasonal variability of lake surface extent
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| Causes of Carryover |
Because of COVID-19, international exchange has been heavily refrained, and we could not hire an international postdoctoral scholar.
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[Journal Article] Global Climate2020
Author(s)
Ades, M. (72/177) H. Kim, et al.
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Journal Title
Bulletin of the American Meteorological Society
Volume: 101
Pages: S9～S128
DOI
Peer Reviewed / Open Access / Int'l Joint Research
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