Modeling the Adverse Impact of Rainstorms on a Regional Transport Network

Cities are centers of socioeconomic activities,and transport networks carry cargoes and passengers from one city to another. However, transport networks are influenced by meteorological hazards, such as rainstorms,hurricanes, and fog. Adverse weather impacts can easily spread over a network. Existing models evaluating such impacts usually neglect the transdisciplinary nature of approaches for dealing with this problem. In this article, a mesoscopic mathematical model is proposed to quantitatively assess the adverse impact of rainstorms on a regional transport network in northern China by measuring the reduction in traffic volume. The model considers four factors: direct and secondary impacts of rainstorms, interdependency between network components, and recovery abilities of cities. We selected the Beijing-Tianjin-Hebei region as the case study area to verify our model.Socioeconomic, precipitation, and traffic volume data in this area were used for model calibration and validation.The case study highlights the potential of the proposed model for rapid disaster loss assessment and risk reduction planning.

Lessons from the Mainland of China’s Epidemic Experience in the First Phase about the Growth Rules of Infected and Recovered Cases of COVID-19 Worldwide

The first phase of the novel coronavirus disease(COVID-19)that emerged at the end of 2019 has been brought under control in the mainland of China in March,while it is still spreading globally.When the pandemic will end is a question of great concern.A logistic model that depicts the growth rules of infected and recovered cases in China’s mainland may shed some light on this question.This model well explained the data by 13 April from 31 countries that have been experiencing serious COVID-2019 outbreaks(R2≥0.95).Based on this model,the semi-saturation period(SSP)of infected cases in those countries ranges from 3 March to 18 June.According to the linear relationship between the growth rules for infected and for recovered cases identified from the Chinese data,we predicted that the SSP of the recovered cases outside China ranges from 22 March to 8 July.More importantly,we found a strong positive correlation between the SSP of infected cases and the timing of a government’s response.Finally,this model was also applied to four regions that went through other coronavirus or Ebola virus epidemics(R2≥0.95).There is a negative correlation between the death rate and the logistic growth rate.These findings provide strong evidence for the effectiveness of rapid epidemic control measures in various countries.

Typhoon Risk Perception: A Case Study of Typhoon Lekima in China

The typhoon is one major threat to human societies and natural ecosystems, and its risk perception is crucial for contextualizing and managing disaster risks in different social settings. Social media data are a new data source for studying risk perception, because such data are timely, widely distributed, and sensitive to emergencies.However, few studies have focused on crowd sensitivity variation in social media data-based typhoon risk perception. Based on the regional disaster system theory, a framework of analysis for crowd risk perception was established to explore the feasibility of using social media data for typhoon risk perception analysis and crowd sensitivity variation. The goal was to quantitatively analyze the impact of hazard intensity and social and geographical environments on risk perception and its variation among population groups. Taking the Sina Weibo data during Typhoon Lekima of 2019 as an example, we found that:(1)Typhoon Lekima-related Weibo public attention changed in accordance with the evolution of the typhoon track and the number of Weibo posts shows a significantly positive correlation with disaster losses, while socioeconomic factors,including population, gross domestic product, and land area, are not explanatory factors of the spatial distribution of disaster-related Weibo posts;(2) Females, nonlocals with travel plans, and people living in areas with high hazard intensity, low elevation, or near waterbodies affected by Lekima paid more attention to the typhoon disaster;and(3)Descriptions of rainfall intensity by females are closer to the meteorological observation data.

Incorporating Triggering and Environmental Factors in the Analysis of Earthquake-Induced Landslide Hazards

Pingwu County of Sichuan Province was severely hit by the 12 May 2008 Wenchuan Earthquake and experienced widely distributed earthquake-induced landslides.We proposed an integrated method that incorporates landslide triggering factors embedded in the Newmark displacement computation and other environmental factors,expressed as lithology,land-use type,vegetation cover(Normalized Difference Vegetation Index,NDVI),elevation,and profile and plan curvature,in the analysis of earthquake-induced landslide hazards in the study area.The earthquake-induced landslide inventory of this area was obtained by visual interpretation of two highresolution SPOT-5 images before and after the earthquake.We used GIS tools to generate an equal number of landslide and non-landslide cell samples in a 30-m grid map,and assigned triggering and environmental variables to each cell.A logistic regression model was built to investigate the occurrence of earthquake-induced landslides.The results show that Newmark displacement(in which triggering factors are embedded)and lithology(as an environmental factor)were the two dominant variables controlling landslide occurrence.Other environmental factors,including NDVI,land-use type,and elevation,also significantly affected landslide occurrences.Overall81.2%correctness was achieved in the regression model.The results confirm the predictive power of our method,which integrates both triggering and environmental factors in modeling earthquake-induced landslides.

Variability in Regional Ecological Vulnerability: A Case Study of Sichuan Province, China

Rapid urbanization and natural hazards are posing threats to local ecological processes and ecosystem services worldwide.Using land use,socioeconomic,and natural hazards data,we conducted an assessment of the ecological vulnerability of prefectures in Sichuan Province for the years 2005,2010,and 2015 to capture variations in its capacity to modulate in response to disturbances and to explore potential factors driving these variations.We selected five landscape metrics and two topological indicators for the proposed ecological vulnerability index(EVI),and constructed the EVI using a principal component analysis-based entropy method.A series of correlation analyses were subsequently performed to identify the factors driving variations in ecological vulnerability.The results show that:(1)for each of the study years,prefectures with high ecological vulnerability were located mainly in southern and eastern Sichuan,whereas prefectures in central and western Sichuan were of relatively low ecological vulnerability;(2)Sichuan’s ecological vulnerability increased significantly(p=0.011)during2005–2010;(3)anthropogenic activities were the main factors driving variations in ecological vulnerability.These findings provide a scientific basis for implementing ecological protection and restoration in Sichuan as well as guidelines for achieving integrated disaster risk reduction.

Fourteen Actions and Six Proposals for Science and Technology-Based Disaster Risk Reduction in Asia Second Asian Science and Technology Conference for Disaster Risk Reduction:SciencePolicy Dialogue for Implementation of the Sendai Framework,April 2018

1 Introduction The Sendai Framework for Disaster Risk Reduction 2015–2030 shifts the focus from managing disasters to reducing risks.Such a shift requires a better understanding of risk in all its dimensions of environment,hazards,exposure,and vulnerability;a disaster risk governance that

Systemic Risks: A Homomorphic Approach on the Basis of Complexity Science

Although the notion of systemic risk gained prominence with respect to financial systems, it is a generic term that refers to risks of increasing importance in many domains—risks that cannot be tackled by conventional techniques of risk management and governance. We build on a domain-overarching definition of systemic risks by highlighting crucial properties that distinguish them from conventional risks and plain disasters. References to typical examples from various domains are included. Common features of systemic risks in different domains—such as the role of agents and emergence phenomena, tipping and cascading, parameters indicating instability, and historicity—turn out to be more than noncommittal empirical observations. Rather these features can be related to fundamental theory for relatively simple and well-understood systems in physics and chemistry. A crucial mechanism is the breakdown of macroscopic patterns of whole systems due to feedback reinforcing actions of agents on the microlevel, where the reinforcement is triggered by boundary conditions moving beyond critical tipping points.Throughout the whole article, emphasis is placed on the role of complexity science as a basis for unifying the phenomena of systemic risks in widely different domains.

Resilience-Driven Road Network Retrofit Optimization Subject to Tropical Cyclones Induced Roadside Tree Blowdown

This article focuses on decision making for retrofit investment of road networks in order to alleviate severe consequences of roadside tree blowdown during tropical cyclones.The consequences include both the physical damage associated with roadside trees and the functional degradation associated with road networks.A trilevel,two-stage,and multiobjective stochastic mathematical model was developed to dispatch limited resources to retrofit the roadside trees of a road network.In the model,a new metric was designed to evaluate the performance of a road network;resilience was considered from robustness and recovery efficiency of a road network.The proposed model is at least a nondeterministic polynomialtime hardness(NP-hard)problem,which is unlikely to be solved by a polynomial time algorithm.Pareto-optimal solutions for this problem can be obtained by a proposed trilevel algorithm.The random forest method was employed to transform the trilevel algorithm into a singlelevel algorithm in order to decrease the computation burden.Roadside tree retrofit of a provincial highway network on Hainan Island,China was selected as a case area because it suffers severely from tropical cyclones every year,where there is an urgency to upgrade roadside trees against tropical cyclones.We found that roadside tree retrofit investment significantly alleviates the expected economic losses of roadside tree blowdown,at the same time that it promotes robustness and expected recovery efficiency of the road network.

Green Development and Integrated Risk Governance

The‘‘International Symposium on Integrated Governance of Large-scale Disaster and Economic Risks’’was held in Qianhai,Shenzhen,China on 13–14 May 2017.The Academy of Disaster Reduction and Emergency Management of Ministry of Civil Affairs and Ministry of Educa-