Vulnerability quantification of roads caused by future debris flows in mountainous areas

Quantifying the vulnerability of roads caused by debris flows is crucial for regional hazard mitigation in remote areas. However, the changing climate has increased the uncertainties in providing reliable vulnerability assessment due to the altered pattern of rainfall. Such change has induced the increased frequency and magnitude of debris flows, impacting the safe operation of highways. In this case, a reliable method was developed to help on the improvement of vulnerability quantification with the involvement of AI and Flo-2D simulation techniques before applying this proposed framework to a case study in the Gyirong Zangbo Basin, Tibet, China. In detail, a deep learning model was developed to estimate the physical vulnerability of roads in the event of a future debris flow with the consideration of a series of factors, including spatial locations of roads to the debris-flow channel, debris-flow catchment area (A_c), length of main channel (L), topographic relief (R), mean slope of main channel (J), and rainfall (P). After that, debris-flow simulations were performed to validate the physical vulnerability assessment results, which can further benefit the accurate quantification of economic loss on a regional scale. Here, in addition to the direct loss of the damaged roads, the indirect loss caused by the damaged roads was also estimated using a complex network theoretical approach that account for regional socioeconomic development and the time needed for road restoration. Overall, this study can form part of an early warning system to assist on the effective management of debris flows on a regional scale in mountainous areas.