Kansei Fujimoto

HS Hydrological Sciences

The 2025 Outstanding Student and PhD candidate Presentation (OSPP) Award is awarded to Kansei Fujimoto for the poster/PICO entitled:

Development of a new satellite rainfall product HiDRED (Himawari Data Rainfall Estimation using Deep learning) and a fundamental study on its applicability to hydrological models (Fujimoto, K.; Tebakari, T.)

Click here to download the poster/PICO file.

Kansei Fujimoto is a PhD candidate at the Graduate School of Science and Engineering, Chuo University in Japan (under the supervision of Professor Dr. Taichi Tebakari). His research focuses on the development of a new satellite-based rainfall dataset (HiDRED) by integrating satellite observations with deep learning techniques. The aim of this work is to provide reliable precipitation data in ungauged regions of Southeast Asia, thereby contributing to practical applications in disaster risk reduction and water resources management.

At EGU2025, his awarded presentation demonstrated the effectiveness of HiDRED through precipitation estimation in actual river basins and subsequent rainfall–runoff analysis. Furthermore, to ensure the social implementation of his research, he is developing a web-based application that will enable government officials and the general public in Southeast Asian countries to access and utilize rainfall information.