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Improving flood and drought prediction using downscaled GRACE terrestrial water storage

Research Project

Project/Area Number 21K20443
Research Category

Grant-in-Aid for Research Activity Start-up

Allocation TypeMulti-year Fund
Review Section 0303:Civil engineering, social systems engineering, safety engineering, disaster prevention engineering, and related fields
Research InstitutionThe University of Tokyo

Principal Investigator

Yin Gaohong  東京大学, 生産技術研究所, 特任研究員 (00906282)

Project Period (FY) 2021-08-30 – 2023-03-31
Project Status Completed (Fiscal Year 2022)
Budget Amount *help
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2022: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2021: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
KeywordsGRACE / TWS / Downscaling / Deep Learning / Flood / Drought / TWSA / LSTM / flood and drought / Machine learning
Outline of Research at the Start

The study propose to downscale GRACE terrestrial water storage (TWSA) using machine learning for flood and drought study. It will provide downscaled TWSA data in both space and time, assimilate TWSA into TE-system, and use downscaled TWSA to extend drought and flood forecasting lead time.

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.

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.

Report

(3 results)
  • 2022 Annual Research Report   Final Research Report ( PDF )
  • 2021 Research-status Report
  • Research Products

    (13 results)

All 2022 Other

All Journal Article (2 results) (of which Int'l Joint Research: 2 results,  Open Access: 2 results,  Peer Reviewed: 1 results) Presentation (6 results) (of which Int'l Joint Research: 3 results,  Invited: 3 results) Remarks (5 results)

  • [Journal Article] Comprehensive analysis of GEO-KOMPSAT-2A and FengYun satellite-based precipitation estimates across Northeast Asia2022

    • Author(s)
      Yin Gaohong, Baik Jongjin, Park Jongmin
    • Journal Title

      GIScience & Remote Sensing

      Volume: 59 Issue: 1 Pages: 782-800

    • DOI

      10.1080/15481603.2022.2067970

    • Related Report
      2022 Annual Research Report 2021 Research-status Report
    • Open Access / Int'l Joint Research
  • [Journal Article] A support vector machine-based method for improving real-time hourly precipitation forecast in Japan2022

    • Author(s)
      Yin Gaohong、Yoshikane Takao、Yamamoto Kosuke、Kubota Takuji、Yoshimura Kei
    • Journal Title

      Journal of Hydrology

      Volume: 612 Pages: 128125-128125

    • DOI

      10.1016/j.jhydrol.2022.128125

    • Related Report
      2022 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Presentation] A support vector machine-based method for improving real-time hourly precipitation forecast in Japan2022

    • Author(s)
      Yoshikane Takao、Yamamoto Kosuke、Kubota Takuji、Yoshimura Kei
    • Organizer
      American Geophysical Union Fall Meeting 2022
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Toward Assimilation of Downscaled Terrestrial Water Storage into Today's Earth for Flood Prediction2022

    • Author(s)
      Yin Gaohong, Yoshimura Kei
    • Organizer
      The Joint PI Meeting of JAXA Earth Observation Missions FY2022
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Towards Assimilation of GRACE Terrestrial Water Storage into a Land Surface Model for Flood and Drought Prediction2022

    • Author(s)
      Yin Gaohong, Yoshimura Kei
    • Organizer
      Land Surface Modeling Summit 2022
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Presentation] The Gravity Recovery and Climate Experiment Mission and Its Application in Hydrology2022

    • Author(s)
      Yin Gaohong
    • Organizer
      Invited Lecture at Lahore University of Management Sciences, Pakistan
    • Related Report
      2022 Annual Research Report
    • Invited
  • [Presentation] A support vector machine-based method for improving real-time hourly precipitation forecast in Japan2022

    • Author(s)
      Gaohong Yin
    • Organizer
      The Joint PI Meeting of JAXA Earth Observation Mission
    • Related Report
      2021 Research-status Report
    • Invited
  • [Presentation] Application of GRACE in Hydrologyl Study2022

    • Author(s)
      Gaohong Yin
    • Organizer
      Lecture at Lahore University of Management Sciences
    • Related Report
      2021 Research-status Report
    • Invited
  • [Remarks] Google Scholar

    • URL

      https://scholar.google.com/citations?user=pkPjO6YAAAAJ&hl=en

    • Related Report
      2022 Annual Research Report
  • [Remarks] Lab Website

    • URL

      https://isotope.iis.u-tokyo.ac.jp/index.php?id=131

    • Related Report
      2022 Annual Research Report
  • [Remarks] Google scholar

    • URL

      https://scholar.google.com/citations?user=pkPjO6YAAAAJ&hl=en

    • Related Report
      2021 Research-status Report
  • [Remarks] Lab website

    • URL

      https://isotope.iis.u-tokyo.ac.jp/index.php?id=131

    • Related Report
      2021 Research-status Report
  • [Remarks] ORCID

    • URL

      https://orcid.org/0000-0002-0234-0688

    • Related Report
      2021 Research-status Report

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Published: 2021-10-22   Modified: 2025-03-27  

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