2023 Fiscal Year Research-status Report
A novel downscaling-integration approach of satellite rainfall estimates for accurate river flow prediction
| Project/Area Number |
22K01031
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| Research Institution | Tottori University |
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Principal Investigator |
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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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Keywords | Satellite rainfall / Rainfall merging / Rainfall erosivity / Rainfall downscaling / Random Forest / Hydrologic modeling / River flow prediction / Soil erosion |
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| Outline of Annual Research Achievements |
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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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
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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| Strategy for Future Research Activity |
(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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