Author(s): Daiwei Cheng; Tadashi Yamada
Linked Author(s):
Keywords: Rainfall-runoff analysis; Stochastic differential equation; Uncertainty of model parameters
Abstract: Serious disasters with enormous damage can change the common recognition of the standard of disaster prevention plans. For example, The Kanto-Tohoku heavy rainfall in September 2015 caused a severe flood disaster in the Kinugawa river basin. Before these severe disasters that occurred in recent years. The Basic standard of disaster prevention plans is to see if a plan can respond to a designed force of certain return period. This is usually called structural countermeasures. However, after these disasters, society now recognized that both structural countermeasures and non-structural countermeasures are necessary. On the other hand, unlike earthquakes and tsunami, there is usually enough time for residents to evacuate in flood disasters if they are appropriately informed. Thus, the prediction of runoff is a critical index for evacuation. To make the prediction, it needs to consider the uncertainty of rainfall intensity and model parameters in the rainfall-runoff analysis. Besides, how to consider the basic nature of uncertainty in the rainfall-runoff system had always been an important topic in hydrology. M.Hino(1974) had first introduced the Kalman filter in forecasting the rainfall - runoff process which considered the uncertainty of the process, since then methods such as Kalman filter, ensemble Kalman filter, particle filter, data assimilation, had been used to consider the uncertainty effects in the rainfall-runoff process. However, these methods are based on filtering theory and statistical methods, which cannot recognize the physical meaning of the uncertainty. The present study is based on the theory of stochas tic differential equation, aimed at suggesting a new way of rainfall-runoff analysis which can not only consider the uncertainty in the system but also identify the physical meaning of these uncertainties
Year: 2020