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将来の環境流量に対する気候変動の影響評価

Research Project

Project/Area Number 17F17372
Research Category

Grant-in-Aid for JSPS Fellows

Allocation TypeSingle-year Grants
Section外国
Research Field Hydraulic engineering
Research InstitutionTokyo Institute of Technology

Principal Investigator

鼎 信次郎  東京工業大学, 環境・社会理工学院, 教授 (20313108)

Co-Investigator(Kenkyū-buntansha) DHANAPALA ARACHC SACHINDRA  東京工業大学, 環境・社会理工学院, 外国人特別研究員
DHANAPALA ARACHCHIGE SACHINDRA  東京工業大学, 環境・社会理工学院, 外国人特別研究員
Project Period (FY) 2017-11-10 – 2020-03-31
Project Status Completed (Fiscal Year 2019)
Budget Amount *help
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 2019: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2018: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2017: ¥100,000 (Direct Cost: ¥100,000)
KeywordsEnvironmental Flow / Climate Change / climate change / environmental flows / statistical modelling / climate change impact / downscaling / environmental flow
Outline of Annual Research Achievements

In the previous fiscal year, it was found that models developed with Parallel Genetic Programming (PGP) algorithm (introduced in this research) show better generalisation skills and produce fewer unphysically large outliers compared to that of models developed with the traditional single deme GP. However, both conventional GP and PGP algorithms were not able to capture the extremes in the observed time series accurately. In a preliminary investigation, it was found that a monthly downscaling approach coupled with a monthly to daily disaggregation method has a better potential to simulate extremes, but the overall performance of the model is not as good as that of a daily downscaling model. Also, disaggregation methods are known to perform poorly when months with similar totals/averages with significantly different intra-monthly distributions are present in observations. In this year, a comprehensive investigation on the improvement to the simulation of extremes in precipitation, temperature and streamflows was conducted focussing on the use of disaggregation. Potential methods to combine the daily simulations with monthly to daily disaggregated simulations were also investigated. Streamflows were projected into the future with the improved methodology, availability of water to satisfy the needs of the environment was investigated. Results were summarized into manuscripts and submitted as papers to journals based on the finding of these new analyses.

Research Progress Status

令和元年度が最終年度であるため、記入しない。

Strategy for Future Research Activity

令和元年度が最終年度であるため、記入しない。

Report

(3 results)
  • 2019 Annual Research Report
  • 2018 Annual Research Report
  • 2017 Annual Research Report
  • Research Products

    (2 results)

All 2019 2018

All Journal Article (1 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 1 results,  Open Access: 1 results) Presentation (1 results) (of which Int'l Joint Research: 1 results)

  • [Journal Article] Machine learning for downscaling: the use of parallel multiple populations in genetic programming2019

    • Author(s)
      D. A. Sachindra, Shinjiro Kanae
    • Journal Title

      Stochastic Environmental Research and Risk Assessment

      Volume: 33 Issue: 8-9 Pages: 1497-1533

    • DOI

      10.1007/s00477-019-01721-y

    • Related Report
      2019 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Presentation] Is parallel evolution of downscaling models using Genetic Programming a way to improve model generalization skills?2018

    • Author(s)
      Sachindra, D.A.
    • Organizer
      International Conference on Environmental and Water Resources Engineering (EWRE 2018)
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research

URL: 

Published: 2017-11-13   Modified: 2024-03-26  

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