A novel downscaling-integration approach of satellite rainfall estimates for accurate river flow prediction

2022 Fiscal Year Research-status Report

• Project/Area Number: 22K01031
• Research Institution: Tottori University
• 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)
• Project Period (FY): 2022-04-01 – 2025-03-31
• Keywords: Satellite rainfall / Rainfall merging / Rainfall erosivity / Rainfall downscaling / Machine learning / Hydrologic modeling / River flow prediction / Soil erosion

Outline of Annual Research Achievements

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).

Current Status of Research Progress

2: Research has progressed on the whole more than it was originally planned.

Reason

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.

Strategy for Future Research Activity

(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.

Causes of Carryover

I plan to do maintenance of existing water level sensors and automatic rain gauges instead of purchasing new ones. River cross-section surveying and development of flow rating curves will be based on recently updated results the the Bureau of Abbay Basin Authority. Also, the travel expenses for the AGU conference participation was covered by fund from other projects. Usage Plane: (i) maintenance of water level sensors and rain gauges, (ii) data collection, (iii) purchase historical rainfall data from Ethiopian meteorological agency, and (iv) travel expenses for fieldwork.

Research Products

• [Presentation] Towards Integrating IMERG-based Global Rainfall Erosivity Estimates with Gauge Data (2022)
  • Author(s): Ayele Almaw Fenta, Atsushi Tsunekawa, Nigussie Haregeweyn, Hiroshi Yasuda, Mitsuru Tsubo, Takayuki Kawai, Mulatu Liyew Berihun, Kindiye Ebabu, Dagnenet Sultan, Ashebir Sewale Belay, Tadesaul Asamin Setargie, Pasquale Borrelli,Panos Panagos
  • Organizer: AGU Fall Meeting
  • Int'l Joint Research