A Novel Distributed Model for Predicting Runoff-Induced Multi-Instability Risk Along Highway Corridors Under Heavy Rainfall
Abstract
The traditional tank model-based landslip early warning system (LEWS) calculates the soil water index (SWI) as a single time series driven by basin-averaged rainfall, which cannot capture spatial heterogeneity along linear highway infrastructures. To overcome this limitation, this study proposes an integrated model that couples the tank model with an ordinary differential equation (ODE) form stormwater runoff simulation model: namely, the distributed runoff model (DRM). The DRM-computed distributed surface water depth replaces the first-layer water height of the tank model to generate spatially varying SWI values. The proposed framework is validated against the 2016 Typhoon No. 10 event that triggered five landslides (L1–L5) along Highway 274 in Hokkaido, Japan. Quantitative results show the following: (1) at all five landslide locations, the peak SWI values exceed 225 mm, while at a non-landslide reference point (L0) the peak SWI is only 158 mm, demonstrating clear spatial differentiation; (2) the predicted landslide initiation times from the integrated model deviate by less than 1.5 h from the actual occurrence times, whereas the shallow water equations (SWEs) and tank-coupled model advances predictions by over 7 h (L3, L4 and L5); (3) after revising the critical line (CL) based on the event data, the proposed model demonstrates a 100% identification rate for the five landslide sites with zero false alarms at L0 in this case study, indicating its potential for practical application. Compared with the tank + SWEs, the proposed tank + DRM approach maintains comparable spatial resolution but significantly improves temporal accuracy and computational efficiency, making it practical for real-time early warning along elongated highway projects. This study provides a spatially differentiated and temporally reliable decision-support tool for rainfall-induced landslide risk assessment along transportation corridors.