International Journal of Disaster Risk Science

2023 Vol. 14, No. 6

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Navigating Interoperability in Disaster Management: Insights of Current Trends and Challenges in Saudi Arabia

Zakaria Ahmed Mani, Mohammed Ali Salem Sultan, Virginia Plummer, Krzysztof Goniewicz

2023, 14(6): 873-885. doi: 10.1007/s13753-023-00528-4

Abstract(11) PDF

Abstract:
In this rapid review, we critically scrutinize the disaster management infrastructure in Saudi Arabia, illuminating pivotal issues of interoperability, global cooperation, established procedures, community readiness, and the integration of cutting-edge technologies. Our exploration uncovers a significant convergence with international benchmarks, while pinpointing areas primed for enhancement. We recognize that continual commitments to infrastructural progression and technology adoption are indispensable. Moreover, we underscore the value of robust community involvement and cross-border collaborations as key factors in bolstering disaster response capabilities. Importantly, we spotlight the transformative influence of emerging technologies, such as artificial intelligence and the Internet of Things, in elevating the effectiveness of disaster management strategies. Our review champions in all-encompassing approach to disaster management, which entails harnessing innovative technologies, nurturing resilient communities, and promoting comprehensive disaster management strategies, encapsulating planning, preparedness, response, and recovery. As a result of our analysis, we provide actionable recommendations to advance Saudi Arabia’s disaster management framework. Our insights are timely and crucial, considering the escalating global focus on disaster response in the face of increasing disaster and humanitarian events.

Community Insights: Citizen Participation in Kamaishi Unosumai Decade-Long Recovery from the Great East Japan Earthquake

Nombulelo Kitsepile Ngulube, Hirokazu Tatano, Subhajyoti Samaddar

2023, 14(6): 886-897. doi: 10.1007/s13753-023-00527-5

Abstract(16) PDF

Abstract:
Numerous scholars and researchers have long advocated for citizen engagement in post-disaster recovery and reconstruction initiatives, although unique opportunities and challenges in effectively implementing citizen engagement still exist. It has been 12 years since the Great East Japan Earthquake, where the government called for a citizen-centered recovery and reconstruction process, and reconstruction in most areas in the Tohoku region has almost been concluded. Using qualitative data acquired through interviews with the residents, field observations during the World Bosai Walk, and questionnaire and archival research, this study aimed to discuss the overall reconstruction of Unosumai in Iwate Prefecture, giving the residents’ perspective on the benefits and challenges they faced in participating in recovery planning and reconstruction and how the community has been able to strengthen their participation in disaster reduction initiatives since the earthquake and tsunami. This discussion is crucial as it would effectively offer lessons on engaging residents in post-disaster recovery and reconstruction after mega-disasters.

Mapping Seismic Hazard for Canadian Sites Using Spatially Smoothed Seismicity Model

Chao Feng, Han-Ping Hong

2023, 14(6): 898-918. doi: 10.1007/s13753-023-00521-x

Abstract(4) PDF

Abstract:
The estimated seismic hazard based on the delineated seismic source model is used as the basis to assign the seismic design loads in Canadian structural design codes. An alternative for the estimation is based on a spatially smoothed source model. However, a quantification of differences in the Canadian seismic hazard maps (CanSHMs) obtained based on the delineated seismic source model and spatially smoothed model is unavailable. The quantification is valuable to identify epistemic uncertainty in the estimated seismic hazard and the degree of uncertainty in the CanSHMs. In the present study, we developed seismic source models using spatial smoothing and historical earthquake catalogue. We quantified the differences in the estimated Canadian seismic hazard by considering the delineated source model and spatially smoothed source models. For the development of the spatially smoothed seismic source models, we considered spatial kernel smoothing techniques with or without adaptive bandwidth. The results indicate that the use of the delineated seismic source model could lead to under or over-estimation of the seismic hazard as compared to those estimated based on spatially smoothed seismic source models. This suggests that an epistemic uncertainty caused by the seismic source models should be considered to map the seismic hazard.

Identifying Neighborhood Effects on Geohazard Adaptation in Mountainous Rural Areas of China: A Spatial Econometric Model

Li Peng, Jing Tan

2023, 14(6): 919-931. doi: 10.1007/s13753-023-00523-9

Abstract(14) PDF

Abstract:
In mountainous rural settlements facing the threat of geohazards, local adaptation is a self-organizing process influenced by individual and group behaviors. Encouraging a wide range of local populations to embrace geohazard adaptation strategies emerges as a potent means of mitigating disaster risks. The purpose of this study was to investigate whether neighbors influence individuals’ adaptation decisions, as well as to unravel the mechanisms through which neighborhood effects exert their influence. We employed a spatial Durbin model and a series of robustness checks to confirm the results. The dataset used in this research came from a face-to-face survey involving 516 respondents residing in 32 rural settlements highly susceptible to geohazards. Our empirical results reveal that neighborhood effects are an important determinant of adaptation to geohazards. That is, a farmer’s adaptation decision is influenced by the adaptation choices of his/her neighbors. Furthermore, when neighbors adopt adaptation strategies, the focal individuals may also want to adapt, both because they learn from their neighbors’ choices (social learning) and because they tend to abide by the majority’s choice (social norms). Incorporating neighborhood effects into geohazard adaptation studies offers a new perspective for promoting disaster risk reduction decision making.

Damage Curves Derived from Hurricane Ike in the West of Galveston Bay Based on Insurance Claims and Hydrodynamic Simulations

Chaoran Xu, Benjamin T. Nelson-Mercer, Jeremy D. Bricker, Meri Davlasheridze, Ashley D. Ross, Jianjun Jia

2023, 14(6): 932-946. doi: 10.1007/s13753-023-00524-8

Abstract(9) PDF

Abstract:
Hurricane Ike, which struck the United States in September 2008, was the ninth most expensive hurricane in terms of damages. It caused nearly USD 30 billion in damage after making landfall on the Bolivar Peninsula, Texas. We used the Delft3d-FM/SWAN hydrodynamic and spectral wave model to simulate the storm surge inundation around Galveston Bay during Hurricane Ike. Damage curves were established through the relationship between eight hydrodynamic parameters (water depth, flow velocity, unit discharge, flow momentum flux, significant wave height, wave energy flux, total water depth (flow depth plus wave height), and total (flow plus wave) force) simulated by the model and National Flood Insurance Program (NFIP) insurance damage data. The NFIP insurance database contains a large amount of building damage data, building stories, and elevation, as well as other information from the Ike event. We found that the damage curves are sensitive to the model grid resolution, building elevation, and the number of stories. We also found that the resulting damage functions are steeper than those developed for residential structures in many other locations.

A Deep Learning Application for Building Damage Assessment Using Ultra-High-Resolution Remote Sensing Imagery in Turkey Earthquake

Haobin Xia, Jianjun Wu, Jiaqi Yao, Hong Zhu, Adu Gong, Jianhua Yang, Liuru Hu, Fan Mo

2023, 14(6): 947-962. doi: 10.1007/s13753-023-00526-6

Abstract(14) PDF

Abstract:
Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency responses. In February 2023, two magnitude-7.8 earthquakes struck Turkey in quick succession, impacting over 30 major cities across nearly 300 km. A quick and comprehensive understanding of the distribution of building damage is essential for efficiently deploying rescue forces during critical rescue periods. This article presents the training of a two-stage convolutional neural network called BDANet that integrated image features captured before and after the disaster to evaluate the extent of building damage in Islahiye. Based on high-resolution remote sensing data from WorldView2, BDANet used pre-disaster imagery to extract building outlines; the image features before and after the disaster were then combined to conduct building damage assessment. We optimized these results to improve the accuracy of building edges and analyzed the damage to each building, and used population distribution information to estimate the population count and urgency of rescue at different disaster levels. The results indicate that the building area in the Islahiye region was 156.92 ha, with an affected area of 26.60 ha. Severely damaged buildings accounted for 15.67% of the total building area in the affected areas. WorldPop population distribution data indicated approximately 253, 297, and 1,246 people in the collapsed, severely damaged, and lightly damaged areas, respectively. Accuracy verification showed that the BDANet model exhibited good performance in handling high-resolution images and can be used to directly assess building damage and provide rapid information for rescue operations in future disasters using model weights.

Identify Landslide Precursors from Time Series InSAR Results

Meng Liu, Wentao Yang, Yuting Yang, Lanlan Guo, Peijun Shi

2023, 14(6): 963-978. doi: 10.1007/s13753-023-00532-8

Abstract(10) PDF

Abstract:
Landslides cause huge human and economic losses globally. Detecting landslide precursors is crucial for disaster prevention. The small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) has been a popular method for detecting landslide precursors. However, non-monotonic displacements in SBAS-InSAR results are pervasive, making it challenging to single out true landslide signals. By exploiting time series displacements derived by SBAS-InSAR, we proposed a method to identify moving landslides. The method calculates two indices (global/local change index) to rank monotonicity of the time series from the derived displacements. Using two thresholds of the proposed indices, more than 96% of background noises in displacement results can be removed. We also found that landslides on the east and west slopes are easier to detect than other slope aspects for the Sentinel-1 images. By repressing background noises, this method can serve as a convenient tool to detect landslide precursors in mountainous areas.

The Dynamics of Interorganizational Collaboration in Disaster Management: A Network Study Based on Flood Disasters in China

Zhichao Li, Long Liu, Shaodan Liu

2023, 14(6): 979-994. doi: 10.1007/s13753-023-00525-7

Abstract(9) PDF

Abstract:
Interorganizational collaboration networks have become an important tool for disaster management. However, research on how different organizations can effectively collaborate throughout the entire disaster management process in centralized states such as China is scarce. This study begins to fill this lacuna by investigating interorganizational collaboration in different phases of disaster management and analyzing changes in the structure of the networks constructed during the preparedness and response phases of the 2020 flood disaster in Hubei Province, China. Building on the complex adaptive systems (CAS) theory, we argue that interorganizational collaboration changes dynamically according to its tasks and requirements. In the preparedness phase, interorganizational collaborations primarily follow established plans and choose horizontal self-organized collaboration mechanisms. However, when the urgent information and resource requirements increase in the response phase, many organizations choose vertical mandatory collaboration mechanisms. We found that organizations at the central and provincial levels in China were well positioned to coordinate information and resources and strengthen the interorganizational collaboration and communication that is crucial in disaster management. These findings contribute to the study of interorganizational collaboration networks in disaster management.

A Generalized Accelerated Failure Time Model to Predict Restoration Time from Power Outages

Tasnuba Binte Jamal, Samiul Hasan

2023, 14(6): 995-1010. doi: 10.1007/s13753-023-00529-3

Abstract(12) PDF

Abstract:
Major disasters such as wildfire, tornado, hurricane, tropical storm, and flooding cause disruptions in infrastructure systems such as power and water supply, wastewater management, telecommunication, and transportation facilities. Disruptions in electricity infrastructure have negative impacts on sectors throughout a region, including education, medical services, financial services, and recreation. In this study, we introduced a novel approach to investigate the factors that can be associated with longer restoration time of power service after a hurricane. Considering restoration time as the dependent variable and using a comprehensive set of county-level data, we estimated a generalized accelerated failure time (GAFT) model that accounts for spatial dependence among observations for time to event data. The model fit improved by 12% after considering the effects of spatial correlation in time to event data. Using the GAFT model and Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates among different types of power companies—investor-owned power companies, rural and municipal cooperatives; (2) the relationship between the duration of power outage and power system variables; and (3) the relationship between the duration of power outage and socioeconomic attributes. The findings of this study indicate that counties with a higher percentage of customers served by investor-owned electric companies and lower median household income faced power outage for a longer time. This study identified the key factors to predict restoration time of hurricane-induced power outages, allowing disaster management agencies to adopt strategies required for restoration process.

Reasoning Disaster Chains with Bayesian Network Estimated Under Expert Prior Knowledge

Lida Huang, Tao Chen, Qing Deng, Yuli Zhou

2023, 14(6): 1011-1028. doi: 10.1007/s13753-023-00530-w

Abstract(12) PDF

Abstract:
With the acceleration of global climate change and urbanization, disaster chains are always connected to artificial systems like critical infrastructure. The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers. The Bayesian network (BN) was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies. To capture the interacting relationships among different factors, a scenario representation model of disaster chains was developed, followed by the determination of the BN structure. In deriving the conditional probability tables of the BN model, we found that, due to the lack of data and the significant uncertainty of disaster chains, parameter learning methodologies based on data or expert knowledge alone are insufficient. By integrating both sample data and expert knowledge with the maximum entropy principle, we proposed a parameter estimation algorithm under expert prior knowledge (PEUK). Taking the rainstorm disaster chain as an example, we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior (MAP) algorithm and the direct expert opinion elicitation method. The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.

A Two-Stage Evolutionary Game Model for Collaborative Emergency Management Between Local Governments and Enterprises

Yanqing Wang, Hong Chen, Xiao Gu

2023, 14(6): 1029-1043. doi: 10.1007/s13753-023-00531-9

Abstract(13) PDF

Abstract:
Enterprises play a vital role in emergency management, but few studies have considered the strategy choices behind such participation or the collaborative relationship with the government. This study contended that enterprises have at least three strategies regarding emergency management: non-participation, short-term participation, and long-term participation. We constructed a two-stage evolutionary game model to explore the behavioral evolution rules and evolutionary stability strategies of the government and enterprises, and employed numerical simulation to analyze how various factors influence the strategy selection of the government and enterprises. The results show that if and only if the utility value of participation is greater than 0, an enterprise will participate in emergency management. The evolutionary game then enters the second stage, during which system stability is affected by a synergistic relationship between participation cost, reputation benefit, and government subsidies, and by an incremental relationship between emergency management benefit, government subsidies, and emergency training cost. This study provides a new theoretical perspective for research on collaborative emergency management, and the results provide important references for promoting the performance of collaborative emergency management.

PERSPECTIVES

The 2023 Earthquake in Türkiye and Implications for China’s Response to Catastrophe

Peijun Shi, Lianyou Liu, Weihua Fang, Jifu Liu, Jidong Wu, Lu Jiang, Bo Chen, Gangfeng Zhang, Hao Zheng, Yintong Zhang

2023, 14(6): 1044-1053. doi: 10.1007/s13753-023-00533-7

Abstract(7) PDF

Abstract:
On 6 February 2023, two 7.8 magnitude earthquakes consecutively hit south-central Türkiye, causing great concern from all governments, the United Nations, academia, and all sectors of society. Analyses indicate that there is also a high possibility of strong earthquakes with a magnitude of 7.8 or above occurring in the western region of China in the coming years. China is a country that is highly susceptible to catastrophic disasters such as earthquakes, floods, and other natural calamities, which can cause significant damages to both human life and property, as well as widespread impacts on the society. Currently, China’s capacity for disaster prevention and control is still limited. In order to effectively reduce the impact of catastrophic disasters, ensure the safety of people’s lives and property to the greatest extent possible, maintain social stability in high-risk areas, and ensure high-quality and sustainable regional development, it is urgent to improve the seismic resistance level of houses and critical infrastructure in high earthquake risk zones and increase the earthquake-resistant design level of houses in high-risk fault areas with frequent seismic activities; significantly enhance the ability to defend against extreme weather and ocean disasters in economically developed areas along the southeastern coast, as well as the level of fortification in response to extreme meteorological and hydrological disasters of coastal towns/cities and key infrastructure; vigorously enhance the emergency response capacity and disaster risk prevention level in western and ethnic minority regions; comprehensively improve the defense level of residential areas and major infrastructure in high geological hazard risk zones with flash floods, landslides, and mudslides; systematically promote national disaster prevention and mitigation education; and greatly enhance the societal disaster risk reduction ability, including catastrophic insurance.