Characterizing the spatial distribution of typical natural disaster vulnerability in China from 2010 to 2017

Wang, Qian; Zhang, Qipeng; Liu, Yangyang; Tong, Linjing; Zhang, Yanzhen; Li, Xiaoyü; Li, Jianlong

doi:10.1007/s11069-019-03656-7

Cited by 22 publications

(9 citation statements)

References 21 publications

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“…The disaster layer does not have an obvious zoning phenomenon. Natural disasters in China are widespread and regional in nature, and the regional nature of the physical geography determines that of natural disasters [24]. Moreover, most of the provinces with greater node correlation are in the main grain-producing regions, meaning that the node provinces in the main grain-producing regions have higher correlation and these provinces are more prone to natural disasters.…”

Section: Further Exploration Of the Three Single-layer Networkmentioning

confidence: 99%

“…Economy and ecology are key factors affecting the regional resilience index [22]. Economic development contributes the most to the reduction of disaster losses [23], meaning that rapid economic and technological development can, to some extent, resist or mitigate natural disasters [24]. In Ethiopia, policy evolution and recent changes that have led to significant progress in addressing grain insecurity are assessed, and the implications and lessons for other developing countries are discussed [25].…”

Section: Introductionmentioning

confidence: 99%

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New Insight into the Coupled Grain–Disaster–Economy System Based on a Multilayer Network: An Empirical Study in China

Jun-li

Wang

et al. 2022

IJGI

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Natural disasters occur frequently causing huge economic losses and reduced grain production. Therefore, it is important to thoroughly explore the spatial correlations between grain, disaster, and the economy. Based on inter-provincial panel data in China in 2019, this study integrates complex network and co-occurrence theory into a coupled grain–disaster–economy (GDE) multilayer network, which provides a new perspective to further explore the spatial correlation between these three systems. We identify the spatial coupled characteristics of the GDE multilayer network using three aspects: degree, centrality, and community detection. The research results show the following: (1) Provinces in the major grain-producing regions have a stronger role in allocating and controlling grain resources, and the correlation between grain and disasters in these provinces is stronger and more prone to disasters. Whereas provinces in the Beijing–Tianjin–Hebei economic zone, and the Yangtze River Delta and Pearl River Delta economic zones, such as Beijing, Tianjin, Jiangsu, Shanghai, and Zhejiang, have a high level of economic development, thereby a stronger ability to allocate economic resources. (2) The economic subsystem assumes a more important, central role compared with the grain and disaster subsystems in the formation and development of the coupled GDE multilayer network, with a stronger coordination for the co-development between the complex grain, disaster, and economy systems in the nodal provinces of the network. (3) The community modularity of the coupled GDE multilayer network is significantly higher than that of the three single-layer networks, indicating a more reasonable community division after coupling the three subsystems. The identification of the spatial characteristics of GDE using multilayer network analysis offers a new perspective on taking various measures to improve the joint sustainable development of grain, disaster, and the economy in different regions of China according to local conditions.

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Section: Further Exploration Of the Three Single-layer Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Insight into the Coupled Grain–Disaster–Economy System Based on a Multilayer Network: An Empirical Study in China

Jun-li

Wang

et al. 2022

IJGI

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“…The middle reaches are found between Yichang city and Hukou county in Jiujiang city, with a length of approximately 955 km. This part is the highly flood-prone area of the Yangtze River (Lai et al, 2017;Wang et al, 2020). The lower reaches cover the Hukou county to the Yangtze River Estuary, with a total length of 938 km (Chen et al, 2001b;Chen et al, 2000).…”

Section: Study Area and Datamentioning

confidence: 99%

Clustering stream profiles to understand the geomorphological features and evolution of the Yangtze River by using DEMs

et al. 2021

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Stream morphology is an important indicator for revealing the geomorphological features and evolution of the Yangtze River. Existing studies on the morphology of the Yangtze River focus on planar features. However, the vertical features are also important. Vertical features mainly control the flow ability and erosion intensity. Furthermore, traditional studies often focus on a few stream profiles in the Yangtze River. However, stream profiles are linked together by runoff nodes, thus affecting the geomorphological evolution of the Yangtze River naturally. In this study, a clustering method of stream profiles in the Yangtze River is proposed by plotting all profiles together. Then, a stream evolution index is used to investigate the geomorphological features of the stream profile clusters to reveal the evolution of the Yangtze River. Based on the stream profile clusters, the erosion base of the Yangtze River generally changes from steep to gentle from the upper reaches to the lower reaches, and the evolution degree of the stream changes from low to high. The asymmetric distribution of knickpoints in the Hanshui River Basin supports the view that the boundary of the eastward growth of the Tibetan Plateau has reached the vicinity of the Daba Mountains.

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“…China is the country that experiences the most frequent natural disasters globally, as it witnesses numerous hazards such as floods, droughts, earthquakes, sandstorms and wildfires every year [1,2]. The landscape of China is characterized by widespread mountainous areas, which makes it highly prone to mass movements [3].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Mixed-Effects Models for Mass Movement Susceptibility Mapping Based on Incomplete Inventory in China

Zhang

2022

Remote Sensing

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Generating an unbiased inventory of mass movements is challenging, particularly in a large region such as China. However, due to the enormous threat to human life and property caused by the increasing number of mass movements, it is imperative to develop a reliable nationwide mass movement susceptibility model to identify mass movement-prone regions and formulate appropriate disaster prevention strategies. In recent years, the mixed-effects models have shown their unique advantages in dealing with the biased mass movement inventory, yet there are no relevant studies to compare different mixed-effects models. This research compared three mixed-effects models to explore the most plausible and robust susceptibility mapping model, considering the inherently heterogeneously complete mass movement information. Based on a preliminary data analysis, eight critical factors influencing mass movements were selected as basis predictors: the slope, aspect, profile curvature, plan curvature, road density, river density, soil moisture, and lithology. Two additional factors, namely, the land use and geological environment division, representing the inventory bias were selected as random intercepts. Subsequently, three mixed-effects models—Statistical-based generalized linear mixed-effects model (GLMM), generalized additive mixed-effects model (GAMM), and machine learning-based tree-boosted mixed-effects model (TBMM)—were adopted. These models were used to evaluate the susceptibility of three distinct types of mass movements (i.e., 28,814 debris flows, 54,586 rockfalls and 108,432 landslides), respectively. The results were compared both from quantitative and qualitative perspectives. The results showed that TBMM performed best in all three cases with AUROCs (Area Under the Receiver Operating Characteristic curve) of cross-validation, spatial cross-validation, and predictions on simulated highly biased inventory, all exceeding 0.8. In addition, the spatial prediction patterns of TBMM were more in line with the natural geomorphological underlying process, indicating that TBMM can better reduce the impact of inventory bias than GLMM and GAMM. Finally, factor contribution analysis showed the key role of topographic factors in predicting the occurrence of mass movements, followed by road density and soil moisture. This study contributes to assessing China’s overall mass movement susceptibility situation and assisting policymakers in master planning for risk mitigation. Further, it demonstrates the tremendous potential of TBMM for mass movement susceptibility assessment, despite inherent biases in the inventory.

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Characterizing the spatial distribution of typical natural disaster vulnerability in China from 2010 to 2017

Cited by 22 publications

References 21 publications

New Insight into the Coupled Grain–Disaster–Economy System Based on a Multilayer Network: An Empirical Study in China

New Insight into the Coupled Grain–Disaster–Economy System Based on a Multilayer Network: An Empirical Study in China

Clustering stream profiles to understand the geomorphological features and evolution of the Yangtze River by using DEMs

Comparison of Three Mixed-Effects Models for Mass Movement Susceptibility Mapping Based on Incomplete Inventory in China

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