Risk analysis of reservoir sedimentation under non‐stationary flows

Abstract Reservoir sustainability is strongly impacted by the reservoir sedimentation processes. Most of the substantial sedimentation processes occur in non‐stationary flows such as in the case of flash floods, surges and tidal waves. However, a stationary probability assumption is normally adopted to reduce mathematical model complexity. This work develops a non‐stationary Gambler's ruin model using the Monte‐Carlo simulation method. Daily water‐level data for the Xia‐Yun station are used to predict the effective risk that the maximum capacity of the water treatment plant in the Shihmen Reservoir is reached. This non‐stationary model yields fairly accurate probabilities of sedimentation by the transitional probability of a reservoir reaching different levels of turbidity, and the average time to reach a designated reservoir maximum handling turbidity. The extended capacity of the proposed model demonstrates the major particle processes during non‐stationary flows. Such analytical results offer water resources agency to scientifically evaluate the dredging/remediation strategies with the existing reservoirs. Transport capacities of rivers and streams, and the potential consequences of flood risks in response to reservoir sedimentation can then be comprehensively estimated in order to allow effective contingency planning for public safety.