| 研究課題/領域番号 |
22K01031
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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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 |
FENTA AYELE・ALMAW 鳥取大学, 国際乾燥地研究教育機構, 特命准教授 (00836984)
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| 研究期間 (年度) |
2022-04-01 – 2025-03-31
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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 |
4,160千円 (直接経費: 3,200千円、間接経費: 960千円)
2024年度: 650千円 (直接経費: 500千円、間接経費: 150千円)
2023年度: 780千円 (直接経費: 600千円、間接経費: 180千円)
2022年度: 2,730千円 (直接経費: 2,100千円、間接経費: 630千円)
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| キーワード | Satellite rainfall / Rainfall merging / Rainfall erosivity / Rainfall downscaling / Random Forest / Hydrologic modeling / River flow prediction / Soil erosion / Machine learning / River flow simulation |
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| 研究開始時の研究の概要 |
The proposed downscaling-integration approach of GPM SREs with hydrological model will be tested in selected watershed of the Upper Blue Nile basin. This study will build on the large databases (e.g., land use, soil, topographic data) we have built in recent years for the Upper Blue Nile basin. In addition, new weather stations and river flow measuring devices will be installed to complement existing data for comprehensive evaluation of downscaled GPM SREs (Objective I) and river flow prediction using GPM SREs (Objective II).
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| 研究実績の概要 |
Global monthly and annual rainfall erosivity were analyzed using long-term (2001-2020), high-temporal-resolution (30-minute) IMERG dataset. In addition, a Random Forest based downscaling and merging of satellite rainfall estimates (IMERG, GSMaP and CHIRPS) and gauge measurements was employed to produce high resolution (1 km) rainfall datasets for the lake Tana basin in Ethiopia.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
Managed to analyze long-term IMERG data and produce monthly and annual global rainfall erosivity datasets. Produced high resolution rainfall dataset integrating IMERG, GSMaP and CHIRPS for the Lake Tana basin. Two papers were published in international journals.
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| 今後の研究の推進方策 |
(1) Maintenance of rainfall and river flow monitoring stations in the Lake Tana basin of Ethiopia. (2) Predict river flow by integrating spatially explicit rainfall data from downscaled and merged (IMERG, GSMaP and CHIRPS) data with hydrological model (SWAT). (3) Evaluate the effect of model calibration approaches, rainfall downscaling and merging schemes on river flow prediction.
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