Landslides, which are a type of process-based geological hazard, exhibit stagewise characteristics that serve as important guidance for the prevention and mitigation of slope engineering disasters. The cross-correlation and randomness of soil parameters can influence the evolution of landslide characteristics. This paper, based on the spatial variability of slope soil parameters, combines copula theory and the material point method (MPM) to establish a Monte Carlo-random material point method considering the cross-correlation of soil parameters. This resulting method is called copula-RMPM. It investigates the probability distributions of slope instability and landslide large deformation characteristics, such as sliding distance, landslide thickness, collapse range, and volume of sliding mass. The results indicated that in the study of soil parameter characteristics, failure probability increases with increased correlation coefficient. Also, failure probability showed a positive correlation with the variability coefficient of cohesion and internal friction angle, with failure probability being more sensitive to the variability coefficient of the internal friction angle. The landslide large deformation characteristics generally follow the normal distribution; they exhibit significant fluctuations in sliding distance and sliding mass area despite the relatively small variability coefficient. Compared with the results of random field simulation of soil parameters, the probability of landslide large deformation characteristics obtained by deterministic soil parameters is often lower. Therefore, the probability distribution of landslide large deformation characteristics obtained by the Monte Carlo-random material point method considering the cross-correlation of soil parameters is more meaningful for engineering guidance.

Compound floods induced by co-occurring multiple drivers may exacerbate the flood impacts and lead to larger flood damage. Exploring future changes in compound flood risk is imperative for flood management and disaster reduction. This study attempts to investigate future changes in compound flood risk across the Yangtze River Basin during 2030 similar to 2100. Future river flow was projected using an improved hydrological model and pairwise series of extremes of rainfall and river flow were extracted from both observed and projected series. Subsequently, stochastic pairs of rainfall and river flow characterizing compound floods were proportionally sampled from their bivariate joint distributions. The damage from each compound flood was obtained from the flood damage function constructed by Random Forests (RF). Further, the expected annual damage (EAD) was calculated to investigate future changes in compound flood risk. Results show that: (1) Future annual maximums of rainfall and river flow are expected to increase by 14.51 % similar to 66.13 % and 1.72 % similar to 55.73 % in the mainstream and northern tributaries, while future annual peak discharge in the southern tributaries (except for the Dongting Lake Basin) is expected to decrease by 4.18 % similar to 12.30 %. A similar spatial distribution of future changes is also found in the bivariate joint distribution of rainfall and river flow. (2) The high coefficient of determination (R-2) of 0.84 indicates the satisfactory simulation and projection capacity of the constructed flood damage function. The positive stepped relationship between flood damage and rainfall or river flow reflects the superposition of multiple flood damage processes. (3) The Han River Basin, the Jialing River Basin, and the two-lakes (the Dongting and Poyang Lakes) area face great threat from compound floods in both historical and future periods. Future compound flood risk is expected to increase by 13.43 % similar to 46.04 % in these regions except for the Poyang Lake Basin, while future risk is expected to increase by 2.03 % similar to 46.04 % in the whole basin. The findings help improve the understanding of future flood risk variations in the Yangtze River Basin and provide essential information for damage reduction.