DEVELOPMENT OF A REAL-TIME RUNOFF PREDICTION SYSTEM USING PARTICLE FILTER AND EFFECT OF INTRODUCING MULTIPOINT DISCHARGE OBSERVATION INFORMATION

Abstract

 A real-time rainfall-runoff prediction system with a particle filter was developed using CommonMP and the effect of introducing multipoint discharge observation information was analyzed. The prediction system consists of nine storage function models with five discharge observation stations, and is applied to the Iwahana River basin (1221 km²) located at the upper part of the Tone River basin. Three data assimilation methods were compared in terms of prediction accuracy. The first method assimilates all storage variables of the storage function method using only the most downstream observed discharge; the second updates the storage variables using all observed discharge; and the third assimilates storage variables in each catchment using the just downstream observed discharge. The third method shows the best performance in terms of accuracy and computational amount.