| 研究課題/領域番号 |
22K01031
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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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| キーワード | Satellite rainfall / Rainfall merging / Rainfall erosivity / Rainfall downscaling / Random Forest / Hydrologic modeling / River flow prediction / Soil erosion |
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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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