Incorporating a trend analysis of large flow perturbations into stochastic modeling of particle transport in open channel flow

Abstract

In extreme flow conditions, both the flow carrying capacity and movement of particles may abruptly change from those associated with regular flows. This study investigates movement of sediment particles in response to extreme flow events using a Lagrangian stochastic jump diffusion particle tracking model (SJD-PTM). The study attempts to investigate the frequency change of extreme flow event occurrences and its impact on suspended sediment particle movement. Using the concept of logistic regression, the trend magnitude of extreme flow events can be used as an input of the proposed stochastic jump diffusion particle tracking model with Logistic regression (SJ-PTM_LR) to account for the potential effects of environmental change. The predicted frequency change of extreme flows from available data in the Chijiawan region in central Taiwan is illustrated in this study. Both ensemble mean and variance of particle trajectory can be quantified under such predicted frequency trend change of extreme flow occurrences via simulations of SJ-PTM_LR. Results show that particle movement uncertainty may undergo a significant increase by taking the effect of the predicted flow frequency trend into consideration. Such probabilistic outcome provides a valuable means for assessing the probability of failure (i.e., risk) resulting from sedimentation processes.