Author(s): Yosuke Nakamura; Shiori Abe

Linked Author(s):

Keywords: Early warning; Initial condition uncertainty; Particle filter; RRI model

Abstract: The increasing frequency of flood damage worldwide due to climate change underscores the urgent need for effective early warning systems. In Japan, flash floods triggered by typhoons or frontal heavy rainfall occur annually in small and medium-sized rivers. This study investigates the improvement of short-range flood forecasts for the Hiwasa River, a typical small mountain river with a 71 km² catchment area. We conducted forecasting experiments for 11 historical flood events and quantitatively evaluated prediction accuracy up to six hours ahead for different rainfall causes. A Rainfall–Runoff–Inundation (RRI) model was employed to simulate hydrological processes, and a particle filter (PF)-based data assimilation approach was introduced to reduce uncertainty in initial moisture conditions. Results indicate that integrating PF with the RRI model improves water level forecasts for all forecast times, particularly within the first three hours. Beyond four hours, the effect of data assimilation diminishes, and underprediction becomes evident. These findings demonstrate that the proposed RRI–PF method enhances short-term flood forecasting accuracy and can contribute to more reliable early warnings for flash floods, supporting timely and safe evacuation of residents.

DOI:

Year: 2026