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 |
応募区分 | 一般 |
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 |
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 (2022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 / Machine learning / Hydrologic modeling / River flow prediction / Soil erosion / River flow simulation |
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 annual and monthly rainfall erosivity was mapped based on a high-temporal-resolution (30-min), long-term (2001-2020) satellite-based precipitation product; the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG); and mean annual rainfall erosivity from the Global Rainfall Erosivity Database (GloREDa) stations (n = 3286). A residual-based merging scheme was applied to integrate GPM-IMERG-based rainfall erosivity with GloREDa using Geographically Weighted Regression. Overall, GPM-IMERG-only estimates underestimated rainfall erosivity. The accuracy of rainfall erosivity estimates from GPM-IMERG merged with GloREDa substantially improved (Nash-Sutcliffe efficiency = 0.83, percent bias = -2.4%, and root mean square error = 1122 MJ mm/ha/h/yr) compared to estimates by GPM-IMERG-only (Nash-Sutcliffe efficiency = 0.51, percent bias = 27.8%, and root mean square error = 1730 MJ mm/ha/h/yr).
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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 GPM-IMERG data and integrated with GloREDa stations for mapping global rainfall erosivity. One paper was presented at international conference. Two manuscripts are prepared for submission to international journals for publication.
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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) Purchase historical rainfall data from the national meteorological agency of Ethiopia. (3) Downscale GPM-IMERG rainfall estimates from 10 km to 1 km using machine learning and cloud properties and land surface characteristics as predictor variables. (4) Predict river flow by integrating spatially explicit rainfall data from downscaled GPM-IMERG with hydrological model (SWAT). (5) Evaluate the effect of model calibration approaches, GPM-IMERG rainfall downscaling and merging on river flow prediction.
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