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Xue D.,State Key Laboratory of Geohazard Prevention and Geoenvironment Protection | Xue D.,Chengdu University of Technology | He Z.,State Key Laboratory of Geohazard Prevention and Geoenvironment Protection | He Z.,Chengdu University of Technology | And 2 more authors.
2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2011 - Proceedings | Year: 2011

A strong earthquake will cause a large number of residential damage, casualties, severely holding back regional economic development. Rapid extraction of information on disasters is critical to acquire the disaster index and disaster assessment, and emergency rapid response can be effective in reducing seismic disaster losses and casualties. Beijing April 14, 2010, Yushu County, Yushu Tibetan Autonomous Prefecture in Qinghai Province, 7.1 magnitude earthquake occurred, as a result of high elevation and strong rainfall after the earthquake, dangerous roads is difficult to walk, even most of the sections of interruption, so it is very difficult for the rescue and assessment. Based on the experience of "5.12" earthquake damage assessment in the paper, to earthquake-stricken areas as the study area of Yushu, a survey and analysis of damaged residential is carried out, to provide technical support for earthquake relief, and it is important reference to damage assessment, post-disaster reconstruction due to similar disasters in the future. © 2011 IEEE. Source

Wang Z.,National Disaster Reduction Center Satellite Application Center for Disaster Reduction of the Ministry of Civil Affairs | Wang Z.,Key Laboratory of Disaster Reduction and Emergency Response | Jiang J.,Key Laboratory of Disaster Reduction and Emergency Response | Jiang J.,Beijing Normal University | And 4 more authors.
Natural Hazards | Year: 2015

Global warming has drawn great attention in recent years, and the resultant extreme drought events have seriously influenced food security. Drought hazard has become a major stress factor for food production in China. The present study aimed to assess the drought hazard risk of maize in middle region of farming-pastoral ecotone of Northern China using spatial maize drought hazard intensity index. The drought hazard intensity index model was set up based on the output variable water stress of environmental policy-integrated climate model and yield loss contribution rate α of water stress in each growth stage. The yield loss contribution rate α is calculated based on the relationship between the water deficit in different stages and the yield loss. Added with the spatial data in time series, results are used for risk assessment. It shows that the tendency of maize drought hazard intensity index was increasing from 1966 to 2011 and was becoming significant after 1996. The volatility is becoming stronger, especially in central basin and northwestern plateau of the study area. The extreme drought events were more frequent. The degree of maize drought was aggravated, and scope of influence was extended after 1999. And the risk of drought hazard is relatively high in the study area, especially in the central basin. In conclusion, this paper presents an effective way to analyze maize drought hazard risk. For the middle region of farming-pastoral ecotone in Northern China, the seriously increasing maize drought hazard is demonstrated by the temporal and spatial analyses and the probability distribution of maize drought hazard intensity index, which presents its sensitivity to climate change and its representativeness to the study of agricultural drought hazard. © 2014, Springer Science+Business Media Dordrecht. Source

Wang Z.,National Disaster Reduction Center | Wang Z.,Key Laboratory of Integrated Disaster Assessment and Risk Governance | He F.,PICC Property and Casualty Company Ltd | Fang W.,The Academy of Management | And 2 more authors.
Natural Hazards | Year: 2013

Food security has drawn great attention from both researchers and practitioners in recent years. Global warming and its resultant extreme drought events have become a great challenge to crop production and food price stability. This study aimed to establish a preliminary theoretical methodology and an operational approach for assessing the physical vulnerability of two wheat varieties ("Yongliang #4" and "Wenmai #6") to agricultural drought using Environmental Policy Integrated Climate model (EPIC). Drought hazard index was set up based on output variables of the EPIC water stress (WS), including the magnitude and duration of WS during the crop-growing period. The physical vulnerability curves of two wheat varieties to drought were calculated by the simulated drought hazard indexes and loss ratios. And the curve's effect on drought disaster risk was defined as A, B and C sections, respectively. Our analysis results showed that (a) physical vulnerability curves varied between two wheat varieties, which were determined by genetic parameters of crops; (b) compared with spring wheat "Yongliang 4#" winter wheat "Wenmai 6#" was less vulnerable to drought under the same drought hazard intensity scenario; (c) the wheat physical vulnerability curve to drought hazard displayed a S shape, suggesting a drought intensity-dependent magnifying or reducing effect of the physical vulnerability on drought disasters; (d) the reducing effect was mainly in the low-value area of vulnerability curve, whereas the magnifying effect was in the middle-value area, and the farming-pastoral zone and the Qinling Mountain-Huaihe River zone formed important spatial division belts. © 2013 Springer Science+Business Media Dordrecht. Source

Dong L.,Capital Normal University | Dong L.,Key Laboratory of Integrated Disaster Assessment and Risk Governance | Hu Z.,Capital Normal University | Hu Z.,Key Laboratory of Integrated Disaster Assessment and Risk Governance
International Conference on Geoinformatics | Year: 2013

Poverty is an important country to solve the livelihood problems. Poverty identification is mainly based on a single income standard. Sen's poverty thinking that poverty is determined by a number of dimensions. Main source of data in the 2000 census in the years 1949-2005 Lan national economic statistics Lan county neighborhood population, the use of multidimensional poverty theory, using the inverse distance weighted interpolation(IDW), the Multidimensional poverty spatial processing of information, research Lan county River Township, East towns and counties, the Lan towns townships multidimensional Poverty level, the weighting factors of each dimension of poverty measurement considering the three dimensions of poverty, and poverty grading classification of thematic mapping. The result shows that: single income dimension of poverty and multidimensional analysis of the extent of poverty is different, and more accurately identify the extent of poverty of the multidimensional. © 2013 IEEE. Source

Hu Z.,Capital Normal University | Hu Z.,Kay Laboratory of Resources Environment and GIS | Hu Z.,Key Laboratory of Integrated Disaster Assessment and Risk Governance | Wei L.,Capital Normal University | And 6 more authors.
Research Journal of Applied Sciences, Engineering and Technology | Year: 2013

In earthquake-stricken area, with the occurrence of aftershocks, heavy rainfall and human activity, the earthquake-induced secondary landslide disaster will threaten people's life and property in a very long period. So, it makes secondary landslide became a research hotspots that draw much attention. The forecasting of natural disaster is considered as a most effective way to prevention or mitigation disaster and the spatial prediction is the base work of landslide disaster research. The aim of this study is to analyze the landslide prediction, taking the case of Beichuan County. Six factors affecting landslide occurrence have been taken into account, including elevation, slope, lithology, seismic intensity, distance to roads and rivers. The correlations of landslide distribution with these factors is calculated, the multiple regression and neural network model are applied to landslide spatial prediction and mapping. The model calculates result is ultimately categorized into four classes. It shows that the high and very high susceptibility areas most distribute in Qushan, Chenjiaba towns, etc., along the rivers and the roads around the area of Longmenshan fault. The precision accuracy using multiple regression models is about 73.7% and the neural network model can be up to 81.28%. It can be concluded that in this study area, the neural network model appears to be more accurate in landslide spatial prediction. © Maxwell Scientific Organization, 2013. Source

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