International Journal of Disaster Risk Science

2023 Vol. 14, No. 1

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Disaster Risk Reduction, Climate Change Adaptation and Their Linkages with Sustainable Development over the Past 30 Years: A Review

Jiahong Wen, Chengcheng Wan, Qian Ye, Jianping Yan, Weijiang Li

2023, 14(1): 1-13. doi: 10.1007/s13753-023-00472-3

Abstract(466) PDF

Abstract:
The severe damage and impacts caused by extreme events in a changing climate will not only make the sustainable development goals difficult to achieve, but also erode the hard-won development gains of the past. This article reviews the major impacts and challenges of disaster and climate change risks on sustainable development, and summarizes the courses and linkages of disaster risk reduction (DRR), climate change adaptation (CCA), and sustainable development over the past 30 years. Our findings show that the conceptual development of DRR actions has gone through three general phases: disaster management in the 1990s, risk management in the 2000s, and resilient management and development in the 2010s. Gradually, CCA has been widely implemented to overcome the adverse effects of climate change. A framework is proposed for tackling climate change and disaster risks in the context of resilient, sustainable development, indicating that CCA is not a subset of DRR while they have similarities and differences in their scope and emphasis. It is crucial to transform governance mechanisms at different levels, so as to integrate CCA and DRR to reduce disaster and climate change risks, and achieve safe growth and a resilient future in the era of the Anthropocene.

Using Extreme Value Theory to Assess the Mortality Risk of Tornado Outbreaks

Vilane Gonçalves Sales, Eric Strobl

2023, 14(1): 14-25. doi: 10.1007/s13753-023-00474-1

Abstract(369) PDF

Abstract:
The majority of tornado fatalities occur during severe thunderstorm occurrences that produce a large number of tornadoes, termed tornado outbreaks. This study used extreme value theory to estimate the impact of tornado outbreaks on fatalities while accounting for climate and demographic factors. The findings indicate that the number of fatalities increases with the increase of tornado outbreaks. Additionally, this study undertook a counterfactual analysis to determine what would have been the probability of a tornado outbreak under various climatic and demographic scenarios. The results of the counterfactual study indicate that the likelihood of increased mortality increases as the population forecast grows. Intensified El Niño events, on the other hand, reduce the likelihood of further fatalities. La Niña events are expected to increase probability of fatalities.

A Vulnerability Assessment Framework for Cultural Heritage Sites: The Case of the Roman Ruins of Tróia

Marvin Ravan, Maria João Revez, Inês Vaz Pinto, Patrícia Brum, Joern Birkmann

2023, 14(1): 26-40. doi: 10.1007/s13753-023-00463-4

Abstract(365) PDF

Abstract:
This article contributes to developing an indicator-based vulnerability assessment framework for cultural heritage sites. It provides a vulnerability index for heritage sites potentially exposed to multiple hazards, including sudden-onset and slow-onset hazards, while considering climate change influences. Through determining particular criteria and indicators, the Cultural Heritage Vulnerability Index incorporates structural and non-structural factors of the heritage site and its local and national settings. The assessment procedure was applied to the case of the Roman Ruins of Tróia in Portugal. The findings highlight those areas of sensitivity (e.g., the existing deterioration patterns and types of foundation) and coping and adaptive capacities (e.g., institutional setting and response plan) that significantly contribute to the level of vulnerability and risk. The results of vulnerability assessment will further enable determining priorities and developing risk mitigation and preparedness measures, in particular reducing structural sensitivity and promoting coping capacities.

Vulnerability Assessment Method for Immovable Cultural Relics Based on Artificial Neural Networks—An Example of a Heavy Rainfall Event in Henan Province

Can Xu, Adu Gong, Long Liang, Xiaoke Song, Yi Wang

2023, 14(1): 41-51. doi: 10.1007/s13753-022-00461-y

Abstract(2055) PDF

Abstract:
Cultural relic conservation capability is an important issue in cultural relic conservation research, and it is critical to decrease the vulnerability of immovable cultural relics to rainfall hazards. Commonly used vulnerability assessment methods are subjective, are mostly applied to regional conditions, and cannot accurately assess the vulnerability of cultural relics. In addition, it is impossible to predict the future vulnerability of cultural relics. Therefore, this study proposed a machine learning-based vulnerability assessment method that not only can assess cultural relics individually but also predict the vulnerability of cultural relics under different rainfall hazard intensities. An extreme rainfall event in Henan Province in 2021 was selected as an example, with a survey report on the damage to cultural relics as a database. The results imply that the back propagation (BP) neural network-based method of assessing the vulnerability of immovable cultural relics is reliable, with an accuracy rate higher than 92%. Based on this model to predict the vulnerability of Zhengzhou City’s cultural relics, the vulnerability levels of cultural relics under different recurrence periods of heavy rainfall were obtained. Among them, the vulnerability of ancient sites is higher than those of other cultural relic types. The assessment model used in this study is suitable for predicting the vulnerability of immovable cultural relics to heavy rainfall hazards and can provide a technical means for cultural relic conservation studies.

Assessing Thai Hospitals’ Evacuation Preparedness Using the Flexible Surge Capacity Concept and Its Collaborative Tool

Phatthranit Phattharapornjaroen, Eric Carlström, Lina Dahlén Holmqvist, Yuwares Sittichanbuncha, Amir Khorram-Manesh

2023, 14(1): 52-63. doi: 10.1007/s13753-023-00468-z

Abstract(174) PDF

Abstract:
According to the concept of “flexible surge capacity,” hospitals may need to be evacuated on two occasions: (1) when they are exposed to danger, such as in war; and (2) when they are contaminated, such as during the Covid-19 pandemic. In the former, the entire hospital must be evacuated, while in the latter, the hospital becomes a pandemic center necessitating the transfer of its non-contaminated staff, patients, and routine activities to other facilities. Such occasions involve several degrees of evacuation—partial or total—yet all require deliberate surge planning and collaboration with diverse authorities. This study aimed to investigate the extent of hospital evacuation preparedness in Thailand, using the main elements of the flexible surge capacity concept. A mixed method cross-sectional study was conducted using a hospital evacuation questionnaire from a previously published multinational hospital evacuation study. The tool contained questions regarding evacuation preparedness encompassing surge capacity and collaborative elements and an open-ended inquiry to grasp potential perspectives. All 143 secondary care, tertiary care, and university hospitals received the questionnaire; 43 hospitals provided responses. The findings indicate glitches in evacuation protocols, particularly triage systems, the inadequacies of surge planning and multiagency collaboration, and knowledge limitations in community capabilities. In conclusion, the applications of the essential components of flexible surge capacity allow the assessment of hospital preparedness and facilitate the evaluation of guidelines and instructions through scenario-based training exercises.

Impact of Spatial Scale and Building Exposure Distribution on Earthquake Insurance Rates: A Case Study in Tangshan, China

Pan Zhang, Xiaojun Li, Chen Liu

2023, 14(1): 64-78. doi: 10.1007/s13753-023-00471-4

Abstract(571) PDF

Abstract:
In order to examine the effect of spatial scale and building exposure distribution on the pure rate of earthquake catastrophe insurance, this study described three modules for rate determination, put forward the general assumptions and principles for calculating the pure insurance rate, and introduced three types of building distribution and their calculation. Taking Tangshan City of Hebei Province in China as an example, we analyzed the pure rate of regional earthquake insurance in terms of spatial scale and building exposure distribution by using the method of control variables. The results show that for districts (or counties) with large differences in seismic risk, the risk areas can be further divided to apply differential rates. In areas with a diverse distribution of potential earthquake source areas and large differences in building density, there is a risk of overestimating or underestimating the pure rate of earthquake insurance when buildings are distributed evenly or partially evenly. This violates the break-even principle of rate setting. This study also provides a reference for earthquake catastrophe insurance companies to choose the spatial scale and the detailed level of exposure distribution in rate determination.

Rapid Prediction Model for Urban Floods Based on a Light Gradient Boosting Machine Approach and Hydrological–Hydraulic Model

Kui Xu, Zhentao Han, Hongshi Xu, Lingling Bin

2023, 14(1): 79-97. doi: 10.1007/s13753-023-00465-2

Abstract(327) PDF

Abstract:
Global climate change and sea level rise have led to increased losses from flooding. Accurate prediction of floods is essential to mitigating flood losses in coastal cities. Physically based models cannot satisfy the demand for real-time prediction for urban flooding due to their computational complexity. In this study, we proposed a hybrid modeling approach for rapid prediction of urban floods, coupling the physically based model with the light gradient boosting machine (LightGBM) model. A hydrological–hydraulic model was used to provide sufficient data for the LightGBM model based on the personal computer storm water management model (PCSWMM). The variables related to rainfall, tide level, and the location of flood points were used as the input for the LightGBM model. To improve the prediction accuracy, the hyperparameters of the LightGBM model are optimized by grid search algorithm and K-fold cross-validation. Taking Haidian Island, Hainan Province, China as a case study, the optimum values of the learning rate, number of estimators, and number of leaves of the LightGBM model are 0.11, 450, and 12, respectively. The Nash-Sutcliffe efficiency coefficient (NSE) of the LightGBM model on the test set is 0.9896, indicating that the LightGBM model has reliable predictions and outperforms random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbor (KNN). From the LightGBM model, the variables related to tide level were analyzed as the dominant variables for predicting the inundation depth based on the Gini index in the study area. The proposed LightGBM model provides a scientific reference for flood control in coastal cities considering its superior performance and computational efficiency.

Spatial Resilience to Wildfires through the Optimal Deployment of Firefighting Resources: Impact of Topography on Initial Attack Effectiveness

Stavros Sakellariou, Athanassios Sfougaris, Olga Christopoulou, Stergios Tampekis

2023, 14(1): 98-112. doi: 10.1007/s13753-023-00464-3

Abstract(175) PDF

Abstract:
Strongly affected by the escalating impacts of climate change, wildfires have been increasing in frequency and severity around the world. The primary aim of this study was the development of specific territorial measures—estimating the optimal locations of firefighting resources—to enhance the spatial resilience to wildfires in the fire-prone region of Chalkidiki Prefecture in northern Greece. These measures focus on the resistance to wildfires and the adaptation of strategies to wildfire management, based on the estimation of burn probability, including the effect of anthropogenic factors on fire ignition. The proposed location schemes of firefighting resources such as vehicles consider both the susceptibility to fire and the influence of the topography on travel simulation, highlighting the impact of road slope on the initial firefighting attack. The spatial scheme, as well as the number of required firefighting forces is totally differentiated due to slope impact. When we ignore the topography effect, a minimum number of fire vehicles is required to achieve the maximization of coverage (99.2% of the entire study area) giving priority to the most susceptible regions (that is, employing 18 of 24 available fire vehicles). But when we adopt more realistic conditions that integrate the slope effect with travel time, the model finds an optimal solution that requires more resources (that is, employing all 24 available fire vehicles) to maximize the coverage of the most vulnerable regions within 27 min. This process achieves 80% of total coverage. The proposed methodology is characterized by a high degree of flexibility, and provides optimized solutions to decision makers, while considering key factors that greatly affect the effectiveness of the initial firefighting attack.

Special Emergency Resources Preallocation Concerning Demand Time for Tunnel Collapse

Xia Li, Yuewen Xiao, Jiaxuan Li, Haipeng Wang, Eryong Chuo, Haili Bai

2023, 14(1): 113-126. doi: 10.1007/s13753-023-00470-5

Abstract(292) PDF

Abstract:
Lacking timely access to rescue resources is one of the main causes of casualties in tunnel collapse. To provide timely rescue, this study proposed a multi-objective preallocation model of special emergency resources for tunnel collapse based on demand time. Efficiency, multiple coverage, and cost-effectiveness are taken as the key objectives of the model; the demand time service range is used as a coverage decision factor considering the unique nature of tunnel collapse. The weight of potential disaster-affected points and other general factors are also considered in this model in order to thoroughly combine the distribution of disaster points and service areas. Further, we take 15 main tunnel projects under construction in China as an example. When the relative proximity to the ideal point of the selected optimal scheme ε_a is smaller than 0.5, we will adjust the weight of three objectives and reselect the optimal scheme until ε_a > 0.5. Compared with the not preallocated case, the number of rescue rigs needed is reduced by 8.3%, the number of covered potential disaster-affected points is increased by 36.36%, the weighted coverage times are increased from 0.853 to 1.383, and the weighted distance is significantly reduced by 99% when the rescue rigs are preallocated, verifying the feasibility and superiority of the proposed model.

Estimating and Mapping Extreme Ice Accretion Hazard and Load Due to Freezing Rain at Canadian Sites

Chao Sheng, Qian Tang, H. P. Hong

2023, 14(1): 127-142. doi: 10.1007/s13753-023-00466-1

Abstract(263) PDF

Abstract:
The ice accretion load in Canadian structural design codes is developed based on an operational ice accretion prediction model. In the present study, three models are employed to predict the ice accretion amount on a flat surface and horizontal wire at Canadian sites. The results confirm that the model used by Canadian practice for predicting ice accretion leads to a conservative estimate as compared to the remaining two models. The results also indicate that the use of the Gumbel distribution for the annual maximum ice accretion is adequate for regions prone to ice accretion and that the lognormal distribution may be considered for regions with a moderate or negligible amount of ice accretion. Maps of the ice accretion hazard at five selected Canadian sites are developed. Statistical analysis of an equivalent wind speed that is concurrent with the iced wire is carried out, showing that the concurrent wind speed for the 50-year return period value of the annual maximum ice accretion amount is smaller than the 50-year return period value of the annual maximum wind speed. It is shown that the statistical characteristics of the annual maximum concurrent wind speed on iced wire differ from that of the annual maximum wind speed.

Hazard Assessment and Hazard Mapping for Kuwait

Ali Al-Hemoud, Abdulla Al-Enezi, Hassan Al-Dashti, Peter Petrov, Raafat Misak, Manar AlSaraf, Mariam Malek

2023, 14(1): 143-161. doi: 10.1007/s13753-023-00473-2

Abstract(229) PDF

Abstract:
Hazard maps are essential tools to aid decision makers in land-use planning, sustainable infrastructure development, and emergency preparedness. Despite the availability of historical data, there has been no attempt to produce hazard maps for Kuwait. In cooperation with the World Bank, this study investigated the natural and anthropogenic hazards that affect Kuwait. The objective was to assess the hazards that face Kuwait and map the hazards of most concern. Hazard maps depicting the spatial distribution of hazard-prone areas are discussed in this article. Hazard assessment maps were generated using multiple datasets and techniques, including meteorological data, satellite imagery, and GIS. Hazard profiling identified a total of 25 hazards, of which five “priority” hazards were explored in detail: (1) surface water flooding; (2) dust storms and sand encroachment; (3) drought; (4) air pollution; and (5) oil spills. The results of this study can aid decision makers in targeting the hazards of most concern. The developed maps are valuable tools for emergency response and hazard mitigation.

SHORT ARTICLE

Resilience Building and Collaborative Governance for Climate Change Adaptation in Response to a New State of More Frequent and Intense Extreme Weather Events

Huiling Ouyang, Xu Tang, Renhe Zhang, Alexander Baklanov, Guy Brasseur, Rajesh Kumar, Qunli Han, Yong Luo

2023, 14(1): 162-169. doi: 10.1007/s13753-023-00467-0

Abstract(186) PDF

Abstract:
The weather conditions of the summer of 2022 were very unusual, particularly in Eastern Asia, Europe, and North America. The devasting impact of climate change has come to our attention, with much hotter and drier conditions, and with more frequent and intense flooding events. Some extreme events have reached a dangerous level, increasingly threatening human lives. The interconnected risks caused by these extreme disaster events are triggering a chain effect, forcing us to respond to these crises through changes in our living environment, which affect the atmosphere, the biosphere, the economy including the availability of energy, our cities, and our global society. Moreover, we have to confront the abnormal consequences of untypical, rapid changes of extreme events and fast switches between extreme states, such as from severe drought to devastating flooding. Recognizing this new situation, it is crucial to improve the adaptation capacity of our societies in order to reduce the risks associated with climate change, and to develop smarter strategies for climate governance. High-quality development must be science-based, balanced, safe, sustainable, and climate-resilient, supported by the collaborative governance of climate mitigation and adaptation. This article provides some recommendations and suggestions for resilience building and collaborative governance with respect to climate adaptation in response to a new planetary state that is characterized by more frequent and severe extreme weather events.