Against the backdrop of the global transition toward clean energy, China's Yangtze River Basin has established the world's largest clean energy corridor. However, the stable operation of the Mega Clean Energy Transmission Network (MCETN) in this region is increasingly threatened by landslides under extreme climate conditions. Given the current lack of clarity regarding the extent of landslide impacts on the MCETN, it is critical to systematically assess the potential spatial distribution probability of landslide occurrence across historical, current (integrating historical periods), and future scenarios. To address this, space-time clustering analysis is used to identify time-aggregation windows in landslide inventories, revealing path-dependent effects in historical landslide occurrences. Next, by integrating multi-temporal environmental factor data and applying two ensemble learning frameworks, the spatial distribution of landslide susceptibility within each temporal window was predicted. Finally, leveraging historical landslide data, future landslide susceptibility (2030−2100) under two climate scenarios (SSP2–4.5 and SSP5–8.5) is assessed. The results demonstrate that landslide events exhibit the strongest clustering within an 8-year window. The blending ensemble framework consistently demonstrates optimal performance across all periods, with annual maximum rainfall contributing most significantly to landslide susceptibility modelling, confirming its role as a primary triggering factor. Integrating landslide susceptibility from different historical periods reveals that 6.7% of the study area falls within a very high level. Interestingly, projections under both climate scenarios indicate that a larger proportion of areas within the MCETN will experience an increase in landslide susceptibility index, highlighting the urgent need to enhance infrastructure resilience against escalating climate extremes.
Space-time variability modelling of landslide susceptibility for strategic infrastructure under changing climate scenarios: The case study of the mega clean energy transmission network (Yangtze River Basin, China)
Peduto D.;Tofani V.
2026
Abstract
Against the backdrop of the global transition toward clean energy, China's Yangtze River Basin has established the world's largest clean energy corridor. However, the stable operation of the Mega Clean Energy Transmission Network (MCETN) in this region is increasingly threatened by landslides under extreme climate conditions. Given the current lack of clarity regarding the extent of landslide impacts on the MCETN, it is critical to systematically assess the potential spatial distribution probability of landslide occurrence across historical, current (integrating historical periods), and future scenarios. To address this, space-time clustering analysis is used to identify time-aggregation windows in landslide inventories, revealing path-dependent effects in historical landslide occurrences. Next, by integrating multi-temporal environmental factor data and applying two ensemble learning frameworks, the spatial distribution of landslide susceptibility within each temporal window was predicted. Finally, leveraging historical landslide data, future landslide susceptibility (2030−2100) under two climate scenarios (SSP2–4.5 and SSP5–8.5) is assessed. The results demonstrate that landslide events exhibit the strongest clustering within an 8-year window. The blending ensemble framework consistently demonstrates optimal performance across all periods, with annual maximum rainfall contributing most significantly to landslide susceptibility modelling, confirming its role as a primary triggering factor. Integrating landslide susceptibility from different historical periods reveals that 6.7% of the study area falls within a very high level. Interestingly, projections under both climate scenarios indicate that a larger proportion of areas within the MCETN will experience an increase in landslide susceptibility index, highlighting the urgent need to enhance infrastructure resilience against escalating climate extremes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
