Probabilistic risk management for agricultural facilities under heavy snowfall: a Markov chain approach considering wet and dry snow conditions

The increasing frequency and intensity of heavy snowfall events due to climate change pose significant risks to agricultural facilities, particularly lightweight structures such as greenhouses. This study develops a probabilistic risk management framework using a Markov Chain approach to analyse snow load dynamics under varying climatic conditions, incorporating the effects of snow density changes due to temperature fluctuations. Time-series meteorological data from 99 weather stations across South Korea over the past 20 years were utilised to calculate hourly snow loads. Cluster analysis was employed to classify snow load states, and transition probabilities between states were derived to construct Markov transition matrices. The framework evaluates failure probabilities of various greenhouse specifications, highlighting the influence of structural thresholds and regional snowfall patterns. Areas prone to heavy snowfall exhibited significantly higher failure probabilities compared to regions with milder conditions. National-scale failure probability maps were developed to provide actionable insights for disaster mitigation, emphasising the importance of region-specific risk management strategies. Results demonstrate the critical role of snow density in snow load evolution and its implications for structural safety. The proposed methodology facilitates early warning systems, resource allocation, and policy recommendations, supporting a proactive approach to disaster risk reduction. This study underscores the necessity of integrating probabilistic models with structural safety assessments to enhance the resilience of agricultural facilities against extreme snowfall events, offering a robust tool for sustainable agricultural practices.