Assessment of Building Physical Vulnerability in Earthquake‑Debris Flow Disaster Chain

Large earthquakes not only directly damage buildings but also trigger debris fows,which cause secondary damage to buildings,forming a more destructive earthquake-debris fow disaster chain.A quantitative assessment of building vulnerability is essential for damage assessment after a disaster and for pre-disaster prevention.Using mechanical analysis based on pushover,a physical vulnerability assessment model of buildings in the earthquake-debris fow disaster chain is proposed to assess the vulnerability of buildings in Beichuan County,China.Based on the specifc sequence of events in the earthquake-debris fow disaster chain,the seismic vulnerability of buildings is 79%,the fow impact and burial vulnerabilities of damaged buildings to debris fow are 92%and 28%respectively,and the holistic vulnerability of buildings under the disaster chain is 57%.By comparing diferent vulnerability assessment methods,we observed that the physical vulnerability of buildings under the disaster chain process is not equal to the statistical summation of the vulnerabilities to independent hazards,which implies that the structural properties and vulnerability of buildings have changed during the disaster chain process.Our results provide an integrated explanation of building vulnerability,which is essential for understanding building vulnerability in earthquake-debris fow disaster chain and building vulnerability under other disaster chains.

Economic Risk Assessment of Future Debris Flows by Machine Learning Method

A reliable economic risk map is critical for effective debris-flow mitigation.However,the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application.To estimate the economic risks caused by future debris flows,a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map.We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year.The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact,temporal probability,and annual susceptibility.We employed a hybrid machine learning model-certainty factor-genetic algorithm-support vector classification-to calculate susceptibilities.Simultaneously,a Poisson model was applied for temporal probabilities,while the determination of annual probability of spatial impact relied on statistical results.Additionally,four major elements at risk were selected for the generation of an economic loss map:roads,vegetation-covered land,residential buildings,and farmland.The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values.Therefore,we proposed a physical vulnerability matrix for residential buildings,factoring in impact pressure on buildings and their horizontal distance and vertical distance to debrisflow channels.In this context,an ensemble model(XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings.The results show that residential buildings occupy 76.7% of the total economic risk,while roadcovered areas contribute approximately 6.85%.Vegetation-covered land and farmland collectively represent 16.45% of the entire risk.These findings can provide a scientific support for the effective mitigation of future debris flows.

Maize drought disaster risk assessment of China based on EPIC model

Digital Agriculture is one of the important applications of Digital Earth.As the global climate changes and food security becomes an increasingly important issue,agriculture drought comes to the focus of attention.China is a typical monsoon climate country as well as an agricultural country with the world’s largest population.The East Asian monsoon has had a tremendous impact upon agricultural production.Therefore,a maize drought disaster risk assessment,in line with the requirements of sustainable development of agriculture,is important for ensuring drought disaster reduction and food security.Meteorology,soil,land use,and agro-meteorological observation information of the research area were collected,and based on the concept framework of‘hazard-inducing factors assessment(hazard)-vulnerability assessment of hazard-affected body(vulner-ability curve)-risk assessment(risk),’importing crop model EPIC(Erosion-Productivity Impact Calculator),using crop model simulation and digital mapping techniques,quantitative assessment of spatio-temporal distribution of maize drought in China was done.The results showed that:in terms of 2,5,10,and 20 year return periods,the overall maize drought risk decreased gradually from northwest to southeast in the maize planting areas.With the 20 year return period,high risk value regions(drought loss rate]0.5)concentrate in the irrigated maize region of Northwest china,ecotone between agriculture and animal husbandry in Northern China,Hetao Irrigation Area,and north-central area of North China Plain,accounting for 6.41%of the total maize area.These results can provide a scientific basis for the government’s decision-making in risk management and drought disaster prevention in China.