Debris flows and flash floods are widely distributed mountain hazards in China. Effective hazard mitigation and prevention require understanding of hazards formation mechanisms and their potential risks. This article elaborates on the formation mechanism, risk analysis, and risk management of debris flows and flash floods. Surface runoff and material supply volume incensement, hazard scale amplification due to outburst of multiple channel blockages and bed erosion as hazard formation mechanism are discussed. Base on the dynamic process of debris flows and flash floods as well as vulnerability assessment of elements at risk, methods of risk assessment and mapping are proposed. Comprehensive engineering and non-engineering measures for hazards control shall be guided by the result of risk analysis that identifies the hazardous level of debris flows and flash floods and incorporates the vulnerability of different elements at risk. Finally, this article discusses strategies when facing risk of these hazards and puts forward a risk management system that involves the participation of local communities.
Under the background of global climate change and urbanization, the risk of heavy rain and floods increases rapidly, which poses great threats on the safety of the global population. It has become a focus in the field of natural disaster risk research to estimate the loss of life induced by heavy rain and floods. Based on the natural disaster system theory, this article first introduces a conceptual framework for estimating the loss of life induced by heavy rain and floods , which includes hazard (H)—exposure units (V)—disaster formative environment (E)—life losses (D). This framework indicates that life loss is a result of the interaction between these multiple elements. Based on existing research in China and internationally, the corresponding indicators and methods are summarized, which are presented in a multidimensional diagram of “impact indicator—loss indicator—analytical method.” This review article considers that the analysis of the multiple impacting factors-life losses relationship is at the core of the estimation of life loss from heavy rain and floods. Three types of methods for estimating the loss of life induced by heavy rain and floods are reviewed, including the vulnerability curve method that focuses on the relationship of H-D,integrated multi-factor analysis method that analyzes the relationship of H-V-E-D, and disaster system simulation method that considers the process of system evolution. Considering the availability of data and effectiveness of methods, no single method can meet the needs of risk assessment of life losses. From the perspective of natural disaster system, quantitative assessment of life loss and risk induced by heavy rain and floods should develop from including single factor to multiple factors; from using statistical analysis of indicators to dynamic simulation of processes; and from employing single method to the integration of multiple methods.
Constructing a spatially-explicit time series of historical land cover dataset is of upmost importance for climatic and ecological studies that make use of land use and cover change (LUCC) data. Some scholars have made efforts to simulate and reconstruct quantitatively information on China's historical land use. Due to the multiple sources of land use data, diversity of reconstruction approaches, and different methods of verification, the reconstruction results of spatially-explicit historical land cover datasets significantly differ. To better understand China's historical land cover datasets and provide effective references for future reconstruction study, it is necessary to comprehensively summarize and discuss the reconstruction methods. By comparing the characteristics of different historical land cover datasets, this research thoroughly analyzed the conceptual constructs, assumptions, reconstruction methods, and validation of the models. The main conclusions are as follows: (1) The majority of the research methods starts with reconstruction of the quantity of historical land cover and then proceeds to spatial pattern reconstruction. Given the lack of adequate and reliable historical data, making reasonable hypotheses about basic data, factors controlling distribution, and limiting factors is an important condition for attaining sound reconstruction results. (2) Methodologically, the majority of the studies uses the reduction method based on historical records and the reconstruction method based on spatial models that consist of the "top-down" method of spatial distribution and the "bottom-up" method of evolutional reconstruction models. (3) In order to improve the explanatory power of the results, it is necessary to validate the reconstruction output of historical land cover datasets in detail. While direct validation of reconstruction results is a more precise method, it is often restricted by the spatiotemporal scales of the research and data sources. Indirect validation method provides an important alternative for accuracy evaluation of the reconstruction results.
This study analyzed the provision and regional difference of four major indoor facilities in residential housing, including tap water, kitchen, toilet, and shower, in Chinese households. Quadrilateral chart method was used to calculate the composite index of provision and regional difference of provision was discussed based on the result. Causes of the detected differences were examined by correlation analysis. At last, policy recommendations were made based on the research findings. The study found that at the county level there was a remarkable regional difference in the provision of the four types of residential housing indoor facilities in China. Their comprehensive conditions differed spatially between the southeastern and northwestern parts of the country as divided by the Hu Huanyong Line that runs from the northeast to the southwest. The composite index values were high in the southeast and low in the northwest. The southeastern part was mainly charactered by very high, high, and medium level provisions. The northwestern part was chiefly featured by low and very low level provisions. There existed significant differences between the eastern, central, western, and northeastern regions. Among the four regions, internal difference was the largest in the eastern region and smallest in the northeastern region. Level of economic development, household income, degree of education, and urbanization rate were important factors influencing the provision of indoor facilities in residential housing.
In recent years, living environment of urban areas in China is attracting increasingly more attention of researchers and urban residents particularly due to the problems caused by rapid economic development. Meanwhile, improving living environment quality is becoming an important target of urban development for the Chinese government. In spite of the increasing number of studies on living environment at smaller scales, few studies have focused on the city scale. Based on a large survey conducted in 2014 in 43 cities of the Bohai Rim area, this study used multilevel modeling, GIS spatial analysis, and multiple linear regression to evaluate the living environment using residents’ subjective perception as indicators, then analyzed the impact of city characteristics on the heterogeneity of the evaluation results. Several conclusions are drawn as follows: (1) There is a significant disparity of evaluation results between the 43 cities. The differences of influencing factors at the city level can explain 20% of the total satisfaction variance. The disparity of living environment quality of cities cannot be neglected in related research on the social, economic, and development issues of cities. Cities in Liaoning Province ranked the highest in the evaluation result while cities in Hebei Province were the worst due to the concentration of massive heavy industries, especially the steel industry. (2) Environment health is the main problem for all the cities that ranked low in the evaluation, which reaffirms that the key point to improve living environment quality is to control environment pollution. (3) Cities with larger land area and population and higher economic development levels normally ranked the lowest with regard to residents’ satisfaction. Satisfaction on living environment is higher in small and medium-sized cities as compared to large cities. Residents living in coastal cities are more satisfied than inland cities. (4) At present, smog control and reduction is key to improving the quality of living environment in the Bohai Rim area.
Under the background of socioeconomic transition and urbanization in China, regional disparity of urban poverty attracts increasingly more attention of Chinese scholars. Based on poverty data from 352 cities (including prefecture-level cities, prefecture-level districts, autonomous prefectures and leagues) from 2007-2011, this study examines the spatial evolution and distribution of urban poverty, as well as the differences between poverty and economic underdevelopment in China. During the five years between 2007 and 2011, the total number and ratio of urban poor have slightly increased. At the regional level, urban population in poverty and areas where poverty situation has aggravated increased sharply in the western region. Most cities in the eastern area have improved in poverty concentration, while the majority of cities in the western area have deteriorated over this period. At the city level, small cities, resource-based cities, and minority population concentrated cities have also deteriorated. In 2011, poverty ratio showed an increasing trend from east to west. Small and medium-size cities, resource-based cities, and minority population concentrated cities had higher poverty ratio. In resource-based cities in the northeast, there were high poverty ratios but the per capita GDP was high, and the areas were characterized by an "invisible urban poverty". In minority population concentrated cities in the southwest, there were low poverty ratios with low per capita GDP, and the areas were characterized by a "low poverty rate urban poverty". In the future, anti-poverty policymaking should take into consideration location, development condition, and demographic characters of cities. Anti-poverty policies should focus on small and medium-size cities, resource-based cities, and minority population concentrated cities in areas where the urban poverty situation is aggravating and balance the relationship between urban poverty reduction and economic development.
：Cities are the most concentrated area of production and consumption activities of the human race, which brings about great amounts of energy consumption and carbon emissions. Therefore, low-carbon city is widely discussed by scholars around the world. In this study, 22 indicators in five areas, including low-carbon development, low-carbon economy, low-carbon environment, city size, and energy consumption, were used to establish an evaluation system for low-carbon city. Remote sensing images of the DMSP-OLS Nighttime Light sets and PM2.5 concentration inversion image were innovatively included in these indicators. Using factor analysis, cluster analysis, and spatial correlation analysis, 284 cities in China were classified as low-carbon cities, comparatively low-carbon cities, comparatively high-carbon cities, and high-carbon cities in 2006 and 2010. The result shows that the low-carbon status of these cities generally improved in 2010 as compared to 2006. According to the driving forces of city development, these cities were divided into four types: environment-oriented, people-oriented, urbanization-dominated, and industry-dominated. Spatially, the Beijing-Tianjin-Hebei area, the Yangtze River Delta region, Shandong Province, and the Pearl River Delta region had the aggregated effect of low-carbon city development. Chongqing, Chengdu, and Wuhan were distinguished from the periphery cities that had lower level of low-carbon development and belonged to the hotspot cities of advanced low-carbon development in Southwest China. Low-carbon development of cities is affected by the administrative level and industrial transformation of cities, among other factors.
This study systematically analyzed the results of current integrated method for social-ecological vulnerability research and found that the comprehensive index method can represent the overall level of social and ecological vulnerabilities using indicators of the two particular aspects of vulnerability in certain time period. But such method cannot reflect the degree of interaction and direction of influence of indicators. Correlation analysis can indicate a quantitative relationship between indicators, but is unable to identify causality. Least square method can generate variable coefficient of explanatory indicators when the indicators have changed, but due to the subjectivity in selecting indicators as explanatory variables or explained variables, the direction of influence between variables still cannot be determined. Given these problems, this article explores the feasibility of using instrumental variable (IV) method to reveal interactions between social and ecological vulnerabilities. By application in a case study at the country level in China , the result shows that the IV method can reveal the direction of influence between indicators of social vulnerability and ecological vulnerability, which overcomes the problem identified above. From 1980s to 2000s, the impact of ecological vulnerability on social vulnerability (NPPI→SoVI) decreased, the corresponding significant impact provinces decreased from 5 to 2; the impact of social vulnerability on ecological vulnerability (SoVI→NPPI) increased, and the corresponding significant impact provinces increased from 1 to 7. A quantitative method that indicates the direction of influence is expected to explain how social vulnerability and ecological vulnerability interact and affect one another.
As an important part of resilience, disaster prevention and mitigation capabilities of the exposure units are a key consideration in disaster economic loss assessment. Based on the data collected from Lizhong Town in the Lixiahe region of Jiangsu Province, the initial depth-damage scatter diagrams and fitted curves were drawn for residential property, industrial property, business, infrastructure, and agriculture. Then the modified depth-damage scatter diagrams and fitted curves in line with the actual damage situation were drawn by taking into consideration disaster prevention and mitigation capabilities. The results show that: (1) At the significance level of 0.05, the correlation between submersion depth and depth-damage rate under different scenarios for residential property, industrial property, business, infrastructure, and agriculture is a power function; (2) Taking into consideration disaster prevention and mitigation capabilities, economic losses of different exposure units caused by flood disaster decreased. Residential property flood losses were reduced by 34% at the submersion depth of 3 m; industrial property losses were reduced by 17% at the submersion depth of 2 m; business losses were reduced by 24% at the submersion depth of 3 m; and infrastructure losses were reduced by 11% at the submersion depth of 2 m. The influence of disaster prevention and mitigation capabilities on residential property flood losses was most obvious. This study may serve as a useful supplement for research on flood depth-damage relation and provide references for disaster prevention and mitigation decisions and disaster risk management in similar areas.
Multi-functionality of land use is the result of the process of land utilization according to the socioeconomic goals, which is the key to ensuring the coordinated development of the socioeconomic and ecological environments. By constructing a social-economic-ecological multi-functional land use evaluation indicator system, this article comprehensively examines the multi-functionality of land use from 1990 to 2013 in Northeast China, which aims to explain the spatiotemporal patterns of multi-functionality of land use in the area. The results show that functional values of land use showed an upward trend during 1990-2013 in Northeast China, but the growth rates differed. The development levels of land use functions in Heilongjiang, Jilin, and Liaoning Provinces were in an ascending order, and the average annual growth rates were 2.976%, 2.725%, and 2.261%, respectively. The social function values of Heilongjiang and Jilin Provinces increased with some fluctuations, but Liaoning Province showed a periodical change. The economic function values of Heilongjiang Province increased, but that of Jilin and Liaoning Provinces showed an overall upward trend yet fluctuated in 2000 to 2005. The ecological function values of Heilongjiang and Jilin Provinces were stable at the beginning of the period then showed an increasing trend with some fluctuations, whereas in Liaoning Province it always showed greater volatility. Land production and transportation, which are important parts of the economic function, had a great impact on the land use functionality of the three provinces in Northeast China. Economic and social factors had a greater impact on the multi-functionality of land use in Heilongjiang and Jilin Provinces, while ecological factors played a smaller role. But in Liaoning Province, economic, social, and ecological factors all had important impacts on the multi-functionality of land use.
Intensity analysis is a popular method as a top-down hierarchical accounting framework to analyze land use maps by considering cross-tabulation matrices and calculating change area and intensity of each land use category in every time interval with the use of remote sensing, geographic information system, and statistical methods. It was applied to analyze land use changes at three levels: temporal (time interval), categorical (land use types), and direction of conversion. At each level, we compared the observed change intensity to a uniform change intensity in order to reveal the characteristics of change. At the temporal level, we identify in which time intervals changes were relatively fast in terms of overall annual change. At the categorical level, we identify which categories were stable as opposed to active relative to the size of the categories. At the direction of conversion level, when a given category gains or loses, we can identify intensive targeting or avoidance of the other categories. In this article, we took land use change in Kunming City as an example to explore area and intensity changes at different levels and analyze stationarity. The results show that temporally land use intensity in Kunming City has gradually increased since 2000 (in the last time interval). With regard to land use categories, built-up areas’ change intensity was most active, while the change intensity of forests was most stable. With regard to the direction of conversion, the increased built-up areas were mainly from previous cropland, while the reduced forest areas were mainly changed into grassland. Intensity analysis theory has great advantages in analyzing land use change processes, which facilitates the mining of change information and understanding of land use change processes and is very useful for scientists.
The GF-1 satellite has advantages such as short revisit period and the combination of various spatial resolutions and large swath, while the Landsat-8 satellite has the advantages of multi-channels and high radiometric resolution. This article presents a case study focused on land cover classification in Zhongxiang City, Hubei Province. Considering the characteristics of different sensor parameters, the Support Vector Machine classifier (SVM) was applied to the two datasets of the same region on 6 August 2013 and a comparison was made on the classification results. The result shows that the coefficient of determination of the corresponding channels between these two sensors are over 0.92. Good consistencies are found in typical samples’ spectrum. However, compared to GF-1, Landsat-8 has better separability between farmland and woodland, and between impervious surface and bare soil. The overall classification accuracy of GF-1 and Landsat-8 reaches 90.38% and 90.07% respectively, whereas there are differences in classification accuracies of different surface types. The differences in spectral response functions may account for the advantage of Landsat-8 on woodland identification accuracy as compared to GF-1. In addition, compared to Landsat-8, GF-1 outperforms in classification accuracy on surface types with fragmented distribution because GF-1 has higher spatial resolution.
