Part I Environments -- Part II Climate changes -- Temperature -- Precipitation -- Wind speed -- Part III: Population and economic system changes -- Population -- Gross domestic Production -- Global crop distribution -- Global industrial value added -- Global road system -- Global population exposure to high temperature -- Global population exposure to rainstorm -- Global GDP exposure to drought -- Global Crop exposure to extremely high temperature -- Part IV Global change risks -- Population risks -- Crop yield risks -- GDP loss risks.
Intro -- Preface -- Contents -- 1 Natural Disaster System in China -- Abstract -- 1 Disaster-Formative Environment -- 1.1 Lithosphere -- 1.2 Atmosphere -- 1.3 Hydrosphere -- 1.4 Biosphere -- 2 Natural Hazards -- 2.1 Diversity of Natural Hazards -- 2.2 Characteristics of Natural Hazards -- 2.3 Regional Differentiation of Natural Hazards -- 3 Exposure Units -- 3.1 Population -- 3.2 Urban Settlements -- 3.3 Transportation System -- 3.4 Economy -- 3.5 Land Use and Land Cover -- 4 Natural Disaster Losses -- 4.1 Disaster-Affected Population -- 4.2 Collapsed Buildings -- 4.3 Agricultural Losses -- 4.4 Direct Economic Losses -- 5 Natural Disaster Risks -- 5.1 Total Risk of Natural Disasters -- 5.2 Risk of Human Casualty from Natural Disasters -- 5.3 Risk of Building Collapse -- 5.4 Risk of Direct Economic Losses -- References -- 2 Earthquake Disasters in China -- Abstract -- 1 Spatial and Temporal Patterns of Earthquakes -- 1.1 Spatial Distribution of Seismic Activities -- 1.2 Temporal Distribution of Seismic Activities -- 2 Formation and Assessment of Earthquake Disasters -- 2.1 Earthquake Disasters and Their Assessment -- 2.2 Fragility of Buildings and Assessment -- 2.3 Earthquake Disaster and Post-disaster Rapid Loss Assessment -- 2.4 Earthquake Disaster Risk Assessment and Mapping -- 2.5 Earthquake Disaster Risk Mapping -- 3 Assessment Result of Earthquake Disaster Risk of China -- 4 Responses to the 2008 Wenchuan Earthquake -- 4.1 Disaster Situation -- 4.2 Emergency Rescue Process -- 4.3 Disaster Response Mechanism -- 4.4 Restoration and Reconstruction -- References -- 3 Landslide and Debris Flow Disasters in China -- Abstract -- 1 Spatial and Temporal Patterns of Landslides and Debris Flows -- 1.1 Spatial Distribution of Landslide and Debris Flow Disaster -- 1.2 Temporal Distribution of Landslide and Debris Flow Disasters -- 1.2.1 Hazards.
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Abstract. Landslides are major hazards that may pose serious threats to mountain communities. Even landslides in remote mountains could have non-negligible impacts on populous regions by blocking large rivers and forming dam-breached mega floods. Usually, there are slope deformations before major landslides occur, and detecting precursors such as slope movement before major landslides is important for preventing possible disasters. In this work, we applied multi-temporal optical remote sensing images (Landsat 7 and Sentinel-2) and an image correlation method to detect subpixel slope deformations of a slope near the town of Mindu in the Tibet Autonomous Region. This slope is located on the right bank of the Jinsha River, ∼80 km downstream from the famous Baige landslide. We used a DEM-derived aspect to restrain background noise in image correlation results. We found the slope remained stable from November 2015 to November 2018 and moved significantly from November 2018. We used more data to analyse slope movement in 2019 and found retrogressive slope movements with increasingly large deformations near the riverbank. We also analysed spatial–temporal patterns of the slope deformation from October 2018 to February 2020 and found seasonal variations in slope deformations. Only the foot of the slope moved in dry seasons, whereas the entire slope was activated in rainy seasons. Until 24 August 2019, the size of the slope with displacements larger than 3 m was similar to that of the Baige landslide. However, the river width at the foot of this slope is much narrower than the river width at the foot of the Baige landslide. We speculate it may continue to slide down and threaten the Jinsha River. Further modelling works should be carried out to check if the imminent landslide could dam the Jinsha River and measures should be taken to mitigate possible dam breach flood disasters. This work illustrates the potential of using optical remote sensing to monitor slope deformations over remote mountain regions.
More than a year after its appearance and still rampant around the world, the COVID-19 pandemic has highlighted tragically how poorly the world is prepared to handle systemic risks in an increasingly hyper-connected global social-ecological system. The absence or clear inadequacy of global governance arrangements and mechanisms is painfully distinct and obvious. In this short article, we summarize a set of COVID-19 pandemic-related analyses and lessons that are inspired by Chinese practice. First, strong government response is one of the most important methods to control a pandemic. Second, countries should be concerned about human-to-frozen goods-to-human transmission. Third, sharing resources and experiences through cooperation is crucial to ensure an adequate health response. Based on these insights, we stress the critical importance of coordination and cooperation, and call for a global network to enhance integrated human health risk resilience.
AbstractThe extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top‐down (or downscaling) approach to disaggregate administrative‐unit level asset value to grid‐cell level. To do so, finding the highly correlated "surrogate" indicators is the key. A combination of three data sets—nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc‐second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time.
The identification of societal vulnerable counties and regions and the factors contributing to social vulnerability are crucial for effective disaster risk management. Significant advances have been made in the study of social vulnerability over the past two decades, but we still know little regarding China's societal vulnerability profiles, especially at the county level. This study investigates the county‐level spatial and temporal patterns in social vulnerability in China from 1980 to 2010. Based on China's four most recent population censuses of 2,361 counties and their corresponding socioeconomic data, a social vulnerability index for each county was created using factor analysis. Exploratory spatial data analysis, including global and local autocorrelations, was applied to reveal the spatial patterns of county‐level social vulnerability. The results demonstrate that the dynamic characteristics of China's county‐level social vulnerability are notably distinct, and the dominant contributors to societal vulnerability for all of the years studied were rural character, development (urbanization), and economic status. The spatial clustering patterns of social vulnerability to natural disasters in China exhibited a gathering–scattering–gathering pattern over time. Further investigations indicate that many counties in the eastern coastal area of China are experiencing a detectable increase in social vulnerability, whereas the societal vulnerability of many counties in the western and northern areas of China has significantly decreased over the past three decades. These findings will provide policymakers with a sound scientific basis for disaster prevention and mitigation decisions.
