Accurate estimation of flood hydrograph using nonlinear Muskingum method: Sequential data assimilation via particle filter

River corridor both in rural and urban environments is a flood-prone area in many parts of the world particularly those where the upstream watershed areas are under extensive urban development. As a result, the flood forecasting and corresponding early warning systems are considered to be mandatory to reduce economic losses and the risk incurred on people. In this paper, nonlinear Muskingum scheme is coupled with a sequential data assimilation updating kernel so called particle filter to refine flood estimation particularly around the peak region. Results of this experiment confirm significant improvement in reference to coupling a flood forecasting scheme with particle updating filter compared to using flood forecasting scheme alone

Keywords:

Flood Forecasting , Nonlinear Muskingum scheme , Particle Filter