Monitoring and Prediction of Hydrological Extreme Events by Satellite Microwave Remote Sensing
| Project/Area Number |
18H03800
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Medium-sized Section 22:Civil engineering and related fields
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| Research Institution | Public Works Research Institute |
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Principal Investigator |
KOIKE Toshio 国立研究開発法人土木研究所, 土木研究所(水災害・リスクマネジメント国際センター), センター長 (30178173)
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| Co-Investigator(Kenkyū-buntansha) |
澤田 洋平 東京大学, 大学院工学系研究科(工学部), 准教授 (30784475)
浅沼 順 筑波大学, 生命環境系, 教授 (40293261)
瀬戸 里枝 東京工業大学, 環境・社会理工学院, 助教 (70799436)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥44,330,000 (Direct Cost: ¥34,100,000、Indirect Cost: ¥10,230,000)
Fiscal Year 2020: ¥14,170,000 (Direct Cost: ¥10,900,000、Indirect Cost: ¥3,270,000)
Fiscal Year 2019: ¥14,690,000 (Direct Cost: ¥11,300,000、Indirect Cost: ¥3,390,000)
Fiscal Year 2018: ¥15,470,000 (Direct Cost: ¥11,900,000、Indirect Cost: ¥3,570,000)
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| Keywords | マイクロ波放射計 / データ同化 / 豪雨 / 渇水 / 水災害 / 気候変動 / 地球観測衛星 / 合成開口レーダ / マイクロ波リモートセンシング / 水循環モデル / 洪水 |
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| Outline of Final Research Achievements |
Two coupled data assimilation systems based on microwave radiometers boarded on satellites were developed and improved to the various fields. One is a coupled atmosphere and land data assimilation system (CALDAS) which aims to improve heavy rainfall prediction accuracy by using a meso-scale numerical weather prediction system including the interactions between land and atmosphere. The other is a coupled land and vegetation data assimilation system (CLVDAS) which aims to improve heavy rainfall prediction accuracy by using a meso-scale numerical weather prediction system including the interactions between land and atmosphere. A spatial down scaling method for the CLVDAS was also developed by coupling with a synthetic aperture radar data.
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| Academic Significance and Societal Importance of the Research Achievements |
国内外で激甚な豪雨災害が頻発し、深刻な渇水災害が長期化している。これらの現象は気候の変化に伴う水循環の極端事象の発生に関する理解や予測と一致しており、その監視・予測能力の向上が喫緊の課題である。本研究では、水循環の極端事象の監視・予測能力を向上して豪雨・渇水被害の軽減を目指して、豪雨については豪雨域の発生場所と強度の同化・予測精度の向上、渇水に関しては農作物被害に直接関係する根系層の土壌水分の推定精度の向上を、地上観測が十分でない地域においても適用可能で、様々な空間なスケールでの適用の可能性を有する衛星データ同化手法の開発と改良、適用に成功した。
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Report
(4 results)
Research Products
(20 results)
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[Journal Article] Uncertainty of Reference Pixel Soil Moisture Averages Sampled at SMAP Core Validation Sites2019
Author(s)
Chen F, Crow WT, Cosh MH, Colliander A, Asanuma J, Berg A, Bosch DD, Caldwell TG, Collins CH, Jensen KH, Martinez-Fernandez J, McNairn H, Starks PJ, Su Z and Walker JP
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Journal Title
Journal of Hydrometeorology
Volume: 20
Issue: 8
Pages: 1553-1569
DOI
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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