Longevity analysis of landslide dams

Abstract

Landslide dams are extremely dangerous because dammed rivers can inundate upstream areas with rising water levels and flood downstream areas after dam breaching. The longevity of landslide dams, which is uncertain, is of great significance for dam failure prevention and mitigation since it determines the time available to take mitigation measures. In this study, the full longevity of landslide dams is divided into three stages (infilling, overflowing and breaching) for better estimation. The influences of dam characteristic parameters (triggers, dam materials and geometric/hydrological parameters) on the full longevity of landslide dams (the period from landslide dam formation to the end of dam failure) as well as on each of the three stages are analysed based on the database. Based on eight dimensionless variables, regression models for estimating the full longevity of landslide dams are developed with a R2 value of 0.781, and regression models for the three-stage longevity (the longevity as the sum of the periods of the three stages) by considering infilling, overflowing and breaching are established with a R2 value of 0.938. It is found that the landslide dam longevity cannot be predicted by one or two influencing factors since it is affected by multiple factors. The relative importance of each control variable is evaluated based on sensitivity analysis: the trigger is the most significant variable in the breaching stage since it affects the size of dam particles, the water content and the inflow rate (e.g. the rainfall trigger results in a larger inflow rate); the lake volume coefficient is more significant in the overflowing stage because it indicates the potential volume of water eroding the dam; and the average annual discharge coefficient is the most important factor in the infilling stage because it controls the time to impound water. The longevity predicted by different models are compared. The models developed in this paper show better accuracy due to the consideration of more parameters based on more cases. In particular, the three-stage longevity regression model shows better accuracy than that of other models because it considers the particular influencing factors for each stage. Three case studies (the “10·10” Baige, Hsiaolin and Tangjiashan landslide dams) are presented to show the application of the regression models developed in this paper. The dam longevity can be predicted more precisely if the timely inflow rate can be estimated by site monitoring or multi-temporal remote sensing images and pre-event digital elevation model (DEM).