Preconditioning Mechanisms and Triggering Processes of Rainfall-Induced Clustered Landslides Controlled by the Coupling Between Landslide-Prone Strata and Micro-Geomorphology

Extreme rainfall in the granitic hilly region of southeastern China often triggers clustered shallow landslides characterized by strong spatial concentration, high density, and near-synchronous occurrence, while exhibiting pronounced differences in failure type. Yet, a unified explanation for why failures aggregate and how different types evolve during the same storm remains limited. Focusing on Xiaba Township as a case study, this work investigates the key predisposing controls and triggering processes of rainfall-induced clustered landslides. Field surveys and geomorphic interpretation indicate that, above shallow surficial residual and weathered layers, the coupled effects of lithology, landform morphology, flow accumulation/convergence, vegetation, and related factors form a shallow, continuous landslide-prone strata (LPS) that is readily mobilized under heavy rainfall, making accurate prediction of LPS burial depth practically important. We compile a point dataset of LPS burial depth from numerous observed landslides and propose a Random-Forest–based ensemble regression framework to address label scarcity and imbalance, spatial autocorrelation, observational noise, and the lack of interpretable uncertainty in conventional approaches. Spatially blocked cross-validation paired with grouped bootstrap resampling, together with robust standardization, mild resampling, and sample weighting, improves the model’s ability to characterize scarce yet critical depth intervals. At inference, a multi-submodel ensemble with Monte Carlo input perturbations yields the median LPS depth and an accompanying uncertainty metric; exceedance-probability curves are used to quantify how predictors alter the probability of surpassing specified depth thresholds. On the validation set, the model achieves Pearson’s r = 0.587, MAE = 0.281 m, RMSE = 0.411 m, and Lin’s CCC = 0.5065, capturing the spatial pattern of LPS burial depth reasonably well; Bland–Altman analysis indicates limits of agreement of about ±0.8 m, mainly at extremes. To link the predicted LPS depth field to geomorphic processes and clustered-failure behavior, we derive ridge-line cross-section metrics from a high-resolution DEM and find that landslides preferentially occur on ridges with larger deflection angles and steeper slopes, with many sites showing signatures of historical reactivation. Spatial topological descriptors of landslide boundaries capture systematic differences between planar- and convergent-type failures and enable robust classification. Building on these insights, we develop a rainfall infiltration–hillslope runoff model that explicitly incorporates geomorphic convergence and apply it to the 16 June storm. Simulations suggest that failures cluster where the LPS approaches saturation and local convergence is high; planar-type landslides activate in a quasi-linear cumulative manner, whereas convergent-type landslides require longer preconditioning before failing abruptly under sustained rainfall. Overall, this field–data–process framework balances accuracy and robustness under imbalance and noise, provides regional LPS-depth mapping with uncertainty, and offers a physically based foundation and parameter constraints for dynamic prediction of clustered landslide risk in granitic hilly terrains.