| 研究課題/領域番号 |
23KF0258
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| 研究種目 |
特別研究員奨励費
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| 配分区分 | 基金 |
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| 応募区分 | 外国 |
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| 審査区分 |
小区分04010:地理学関連
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| 研究機関 | 鳥取大学 |
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研究代表者 |
Haregeweyn N 鳥取大学, 国際乾燥地研究教育機構, 教授 (30754692)
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| 研究分担者 |
ALEMU DAGNENET 鳥取大学, 国際乾燥地研究教育機構, 外国人特別研究員
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| 研究期間 (年度) |
2023-11-15 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
2,000千円 (直接経費: 2,000千円)
2025年度: 500千円 (直接経費: 500千円)
2024年度: 1,000千円 (直接経費: 1,000千円)
2023年度: 500千円 (直接経費: 500千円)
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| キーワード | Peak flow / Lag time / Time of concentration / Machine learning / Sediment concentration |
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| 研究開始時の研究の概要 |
This research aims to get insights into the diverse hydrological response time estimation methods employed across different climatic regions. This can be achieved through global scale review of literatures complimenting with analysis of peak river flow and suspended sediment observations from 15 contrasting tropical watersheds found in Ethiopia. Ultimately it aims to develop accurate regional-scale flow and sediment response-time estimation Machine Learning (ML) models.
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| 研究実績の概要 |
Global Hydrological response time estimation methods were reviewed and archived from 80 published articles to survey and evaluate the accuracy of existing methods across different climatic region. Relationship between catchment size, slope, rainfall intensity, dominant soil texture and land use versus measured time of concentration were analyzed. Additionally, event-based rainfall-peak flow data maintained from our previous study under contrasting climates of Ethiopia were analyzed to determine lag time of peak flows and time of concentration. To support these activities satellite images and laptop computer were purchased.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
The necessary preparations to conduct the field work in Ethiopia in the coming summer are slightly delayed due to security travel restriction to the study sites. Efforts are being made to accelerate the preparation so that the necessary data could be collected as per the initial plan.
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| 今後の研究の推進方策 |
The future research plan comprises four key activities: 1) Conducting rainy season field surveys in Ethiopia to collect data on peak flow, soil moisture, and sediment concentration for hydrological model validation. 2) Compiling biophysical, hydrological, and meteorological data from various sources to enhance the watershed database. 3) Evaluating the accuracy of existing methods for estimating hydrological response times across different climates. 4) Developing machine learning models to predict flood and sediment response times in tropical regions, enhancing predictive accuracy and efficiency
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