Heavy snowfall events pose significant operational challenges for emergency response systems in mountainous regions, where simultaneous disruptions to road infrastructure and surges in demand for emergency resources create a critical paradox: logistical capacity is most constrained precisely when it is most needed. Although international frameworks for emergency route designation exist (Japan, USA, Canada), systematic methodologies for identifying routes that maintain accessibility under extreme snowfall conditions remain limited. This study presents a practical GIS-based multi-criteria decision framework for pre-designating emergency resource mobilization routes by integrating the Analytic Hierarchy Process (AHP) with Dijkstra’s shortest-path algorithm. It systematically addresses Common Cause Failure (CCF), which occurs when multiple routes fail simultaneously due to shared geometric vulnerabilities. The framework operationalizes three core principles: mobility (hierarchical routing), connectivity (CCF avoidance), and redundancy (alternative routes). Applied to Gangneung City, South Korea, a representative case for snowfall-vulnerable mountainous regions globally, expert elicitation involving 60 transportation and disaster management professionals derived AHP-based criteria weights across three evaluation dimensions: geometric road characteristics, operational performance attributes, and functional road classification. Results indicate that geometric characteristics, particularly carriageway width and longitudinal gradient, dominate route suitability, accounting for 48.0
The intensification of climate extremes and the increasing interdependence of drought types have challenged conventional understandings of drought risk, especially in large-scale basins. Yet most existing studies rely on correlation-based analyses, which are insufficient to reveal the dynamic causality and feedback mechanisms underlying compound and cascading droughts. Focusing on the Yangtze River Basin (YRB) during 1954–2021, this study introduces an extended Convergent Cross Mapping (extended CCM) approach to construct a causal propagation framework among meteorological (M-drought), hydrological (H-drought), and agricultural droughts (A-drought). The results reveal that M-drought is the primary initiating driver, triggering H-drought and A-drought with typical lags of 1–3 months and forming distinct amplification cascades. Remarkably, a reverse A-drought-to-M-drought linkage is detected in ecologically sensitive regions, suggesting a possible soil vegetation–climate feedback and challenging traditional unidirectional assumptions. Compound droughts display pronounced spatial heterogeneity: meteorological-agricultural droughts (MA-drought) are the most spatially extensive, whereas meteorological-hydrological-agricultural droughts (MHA-drought) exhibit high intensity but limited coverage. By moving beyond correlation to a causality-informed framework, this study offers new insights into the spatiotemporal dynamics of multi-type droughts and provides a scientific basis for early warning and adaptive water resource management in complex basin systems.
Global climate change has amplified the frequency and severity of extreme weather events, presenting critical challenges for the safe operation of railway transportation systems. There is an urgent need to identify the critical elements of resilience management under natural hazards to ensure continuous transportation services. This study developed a dynamic Bayesian network (DBN)-based resilience assessment framework to characterize resilience variations of rail systems under extreme weather conditions. The proposed model demonstrated an empirical representation of meteorological disasters and introduced refined assessment metrics to improve its practical applicability. The real-world rail system was employed as a case study to analyze and compare resilience evolution under short-term and long-term meteorological disruptions. The results offer a better quantitative understanding of railway system resilience dynamics under extreme weather conditions. Furthermore, the sensitivity analysis of model parameters indicates that reducing the failure rate during disruptions and improving the system’s internal repair rate are critical strategies for building resilient railway transportation systems. The concluding remarks provide actionable insights for rail administrations and policymakers to optimize decision-making and emergency management practices, contributing to railway safety enhancement.
Santos and Guarujá have a long history of shallow landslides, with rupture surfaces generally reaching depths of up to 3 m. The slopes are underlain by gneissic and migmatitic bedrocks, where failures occur in immature residual soils, often at the soil–saprolite interface and exacerbated by anthropogenic interventions. An extreme rainfall event in March 2020 triggered a major disaster, resulting in 41 fatalities. This study investigates the relationship between landslides and rainfall using simple and effective half-life rainfall indices over a 5-year period (2018–2022). Rainfall accumulated over 1, 12 and 96 h was computed using the Automatic Rainfall Half-Life Cluster System (ARHCS), while K-means clustering and threshold identification were performed. The dataset comprised 136 landslide rainfall indices in Santos, 178 in Guarujá, and 665 non-occurrence rainfall events. The results show that effective half-life rainfall indices reduce data dispersion compared with simple rainfall indices, particularly when analyzed using K-means clustering. They also identify distinct rainfall reference levels from moderate to extreme, associated with induced, occasional, multiple, and generalized landslide scenarios. The established threshold levels are consistent with the empirical curves adopted by the São Paulo Preventive Civil Defense Plan, while soil moisture data from CEMADEN stations provide additional support for interpreting the landslide-rainfall relationship. The study demonstrates that the application of the Effective Half-Life method to landslide forecasting improves the definition of rainfall conditions associated with occurrence and non-occurrence rainfall events. Validation of the results indicated better predictive performance for the Half-Life method (98
Climate change is modifying the rainfall conditions that trigger landslides, threatening road networks essential for economic development and disaster response. This systematic literature review with descriptive bibliometrics analyses 80 publications (2009–2025) from Scopus and Web of Science. Unlike previous reviews, which address landslide–climate interactions without an infrastructure focus or road-network vulnerability without a climate driver, it requires the simultaneous presence of four domains: landslide processes, road infrastructure, risk assessment, and weather or climate drivers. A standardized extraction form recorded region, scale, method, climate input, risk components and indirect impacts for every study. Output grew by 11
