Spatiotemporal evolution and driving factors of China’s flash flood disasters since 1949

2018 ◽  
Vol 61 (12) ◽  
pp. 1804-1817 ◽  
Author(s):  
Yesen Liu ◽  
Zhenshan Yang ◽  
Yaohuan Huang ◽  
Changjun Liu
Keyword(s):  
Flash Flood ◽  
Driving Factors ◽  
Spatiotemporal Evolution ◽  
Flood Disasters

scholarly journals Geomatic-Based Flood Loss Assessment and Its Application in an Eastern City of China

Water ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 126
Author(s):  
Youjie Jin ◽  
Jianyun Zhang ◽  
Na Liu ◽  
Chenxi Li ◽  
Guoqing Wang
Keyword(s):  
Spatial Resolution ◽  
Loss Rate ◽  
Flash Flood ◽  
Economic Loss ◽  
Economic Losses ◽  
Risk Level ◽  
Loss Assessment ◽  
Flood Loss ◽  
Flood Disasters

Flash-flood disasters pose a serious threat to lives and property. To meet the increasing demand for refined and rapid assessment on flood loss, this study exploits geomatic technology to integrate multi-source heterogeneous data and put forward the comprehensive risk index (CRI) calculation with the fuzzy comprehensive evaluation (FCE). Based on mathematical correlations between CRIs and actual losses of flood disasters in Weifang City, the direct economic loss rate (DELR) model and the agricultural economic loss rate (AELR) model were developed. The case study shows that the CRI system can accurately reflect the risk level of a flash-flood disaster. Both models are capable of simulating disaster impacts. The results are generally consistent with actual impacts. The quantified economic losses generated from simulation are close to actual losses. The spatial resolution is up to 100 × 100 m. This study provides a loss assessment method with high temporal and spatial resolution, which can quickly assess the loss of rainstorm and flood disasters. The method proposed in this paper, coupled with a case study, provides a reliable reference to loss assessment on flash floods caused disasters and will be helpful to the existing literature.

scholarly journals China National Flash Flood Disasters Investigation and Assessment

10.29007/7zjd ◽  
2018 ◽  
Author(s):  
Xiaolei Zhang ◽  
Liang Guo ◽  
Ronghua Liu ◽  
Qi Liu ◽  
Qiuling Yao ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Flood Control ◽  
Flash Flood ◽  
Site Investigation ◽  
Flood Management ◽  
Information Support ◽  
Future Application ◽  
Human Settlement ◽  
Rainfall Index ◽  
Flood Disasters

National Flash Flood Disasters Investigation and Assessment project is the largest non-engineering projects in water conservancy industry in China, and also the largest scale of general census on disasters’ background in flood management and mitigation fields. Through general census, on-site investigation, field measurement, hydrological analysis and calculation, the spatial distribution, human settlement, underground situations, social and economic impacts, hazard zoning, warning indicators of flash flood disasters were collected, the storm flood characters in mountainous areas were analyzed, the flood control ability of selected villages were assessed, the critical rainfall index of these villages were obtained, and the hazard zones were finally identified, all of which provided a strong information support for flash flood early-warning and forecast and residential safety transfer. This paper systematically introduced the key technical focuses, made a general review on the data and information collected, and discussed the spatial distribution pattern of these elements. Based on these survey data, the characteristics of flash flood disaster prevention areas, the human settlement features and storm flood spatial distribution situation were further analyzed. In the end of this paper, future application and analysis on diversified utilization of national flash flood disasters investigation and assessment results were proposed.

scholarly journals Community Preparation in Tangse District in Flash Flood Disaster Mitigation

2020 ◽  
Vol 7 (3) ◽  
pp. 368
Author(s):  
Martini Martini ◽  
Azmeri Azmeri ◽  
Didik Sugiyanto
Keyword(s):  
Law Enforcement ◽  
Research Design ◽  
Flash Flood ◽  
Disaster Mitigation ◽  
Design Data ◽  
Data Presentation ◽  
Qualitative Research Design ◽  
Flood Disasters ◽  
Law Enforcement Officials ◽  
Knowledge And Attitude

This study aims to determine the Tangse community preparedness in mitigation of flood disasters. The research design used in this study is a qualitative research design . Data analysis is performed using three activities that occur simultaneously, namely data reduction, data presentation, and drawing conclusions or verification, or can also use words to describe facts and observed phenomena. Based on the results of research and discussion, it can be concluded that in terms of the knowledge and attitude of the Tangse community it is already good where the community already knows the signs of flash floods and the environment is vulnerable to disasters. But it is still very lacking in terms of regulations and policies. Regulations that have not been implemented properly, as well as sanctions for illegal loggers involving law enforcement officials. 

scholarly journals Spatiotemporal clustering of flash floods in a changing climate (China, 1950–2015)

2020 ◽  
Author(s):  
Nan Wang ◽  
Luigi Lombardo ◽  
Marj Tonini ◽  
Weiming Cheng ◽  
Liang Guo ◽  
...  
Keyword(s):  
Flash Flood ◽  
Extreme Rainfall ◽  
Flash Floods ◽  
Scan Statistics ◽  
Spatiotemporal Pattern ◽  
Precipitation Trend ◽  
Wet Season ◽  
Extreme Rainfall Events ◽  
Space And Time ◽  
Flood Disasters

Abstract. The persistence over space and time of flash flood disasters – flash floods that have caused either economical or life losses, or both – is a diagnostic measure of areas subjected to hydrological risk. The concept of persistence can be assessed via clustering analyses, performed here to analyse the national inventory of flash flood disasters in China occurred in the period 1950–2015. Specifically, we investigated the spatiotemporal pattern distribution of the flash flood disasters and their clustering behavior by using both global and local methods: the first, based on the Ripley's K-function, and the second on Scan Statistics. As a result, we could visualize patterns of aggregated events, estimate the cluster duration and make assumptions about their evolution over time, also with respect precipitation trend. Due to the large spatial (the whole Chinese territory) and temporal (66 years) scale of the dataset, we were able to capture whether certain clusters gather in specific locations and times, but also whether their magnitude tends to increase or decrease. Overall, the eastern regions in China are much more subjected to flash flood disasters compared to the rest of the country. Detected clusters revealed that these phenomena predominantly occur between July and October, a period coinciding with the wet season in China. The number of detected clusters increases with time, but the associated duration drastically decreases in the recent period. This may indicate a change towards triggering mechanisms which are typical of short-duration extreme rainfall events. Finally, being flash flood disasters directly linked to precipitation and their extreme realization, we indirectly assessed whether the magnitude of the trigger itself has also varied through space and time, enabling considerations in the context of climatic changes.

scholarly journals Damage assessment in Braunsbach 2016: data collection and analysis for an improved understanding of damaging processes during flash floods

2017 ◽  
Vol 17 (12) ◽  
pp. 2163-2179 ◽  
Author(s):  
Jonas Laudan ◽  
Viktor Rözer ◽  
Tobias Sieg ◽  
Kristin Vogel ◽  
Annegret H. Thieken
Keyword(s):  
Risk Management ◽  
Data Collection ◽  
Damage Assessment ◽  
Structural Damage ◽  
Flash Flood ◽  
Multinomial Logistic Regression ◽  
Flash Floods ◽  
Driving Factors ◽  
Ex Post ◽  
Damage Grades

Abstract. Flash floods are caused by intense rainfall events and represent an insufficiently understood phenomenon in Germany. As a result of higher precipitation intensities, flash floods might occur more frequently in future. In combination with changing land use patterns and urbanisation, damage mitigation, insurance and risk management in flash-flood-prone regions are becoming increasingly important. However, a better understanding of damage caused by flash floods requires ex post collection of relevant but yet sparsely available information for research. At the end of May 2016, very high and concentrated rainfall intensities led to severe flash floods in several southern German municipalities. The small town of Braunsbach stood as a prime example of the devastating potential of such events. Eight to ten days after the flash flood event, damage assessment and data collection were conducted in Braunsbach by investigating all affected buildings and their surroundings. To record and store the data on site, the open-source software bundle KoBoCollect was used as an efficient and easy way to gather information. Since the damage driving factors of flash floods are expected to differ from those of riverine flooding, a post-hoc data analysis was performed, aiming to identify the influence of flood processes and building attributes on damage grades, which reflect the extent of structural damage. Data analyses include the application of random forest, a random general linear model and multinomial logistic regression as well as the construction of a local impact map to reveal influences on the damage grades. Further, a Spearman's Rho correlation matrix was calculated. The results reveal that the damage driving factors of flash floods differ from those of riverine floods to a certain extent. The exposition of a building in flow direction shows an especially strong correlation with the damage grade and has a high predictive power within the constructed damage models. Additionally, the results suggest that building materials as well as various building aspects, such as the existence of a shop window and the surroundings, might have an effect on the resulting damage. To verify and confirm the outcomes as well as to support future mitigation strategies, risk management and planning, more comprehensive and systematic data collection is necessary.

scholarly journals Spatiotemporal Evolution Pattern and Driving Factors of Higher-Education Population in China

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Qiudi Zhao ◽  
Yaohuan Huang ◽  
Yesen Liu
Keyword(s):  
Higher Education ◽  
Population Density ◽  
Total Population ◽  
Driving Forces ◽  
Driving Factors ◽  
City Size ◽  
Spatiotemporal Evolution ◽  
Evolution Pattern ◽  
Sampling Statistics ◽  
The City

The spatial and temporal distribution of the higher-education population (HEP) is a fundamental characteristic of the development level of higher education in a region or a country. Based on the annual population sampling statistics from 2000 to 2015, the spatiotemporal evolution pattern of the HEP in China is systematically analyzed. Meanwhile, 9 driving factors related to natural conditions and socioeconomic conditions of average slope, average elevation, the city location, the city size, high-speed railways, highways, gross domestic product (GDP) density, nonagricultural population, and population density of 2000 and 2010 at the municipal level are constructed. Then, the factors driving the distribution of the HEP are quantitatively analyzed using the geodetector model. The results show that the centroid of the HEP, shifting from the northeast to the southwest from 2000 to 2010, is markedly different from that of the total population from 2000 to 2015 in China. Despite their different moving directions, the distance between the two centroids is decreasing, indicating both significant regional differences of the HEP in China and a narrowing gap between the HEP and the total population in recent years. The results of the factor detector of 2000 and 2010 suggest that the proportion of the nonagricultural population and the city location are the main driving factors of the distribution of the HEP, with driving forces between 0.494 and 0.627, followed by the city size, highways, and GDP density, with driving forces are between 0.199 and 0.302. It indicates that urbanization levels and urban locations are the main factors affecting the spatial distribution of the HEP. The results of the interaction detection reveal that the interaction of the nonagricultural population and the GDP density can explain 92.7% of the spatial variety of the HEP in 2000, while that of the nonagricultural population and the population density can explain 97.6% of the spatial variety of the HEP in 2010, which reflects a more balanced development of the HEP. In addition, a large proportion of the HEP transfers from economically developed areas to densely populated areas.

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