2019 Vol. 10, No. 3

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A Dilemma of Language: “Natural Disasters” in Academic Literature
Ksenia Chmutina, Jason von Meding
2019, 10(3): 283-292. doi: 10.1007/s13753-019-00232-2
For decades sections of the academic community have been emphasizing that disasters are not natural. Nevertheless, politicians, the media, various international organizations-and, more surprisingly, many established researchers working in disaster studies-are still widely using the expression "natural disaster." We systematically analyzed the usage of the expression "natural disaster" by disaster studies researchers in 589 articles in six key academic journals representative of disaster studies research, and found that authors are using the expression in three principal ways:(1) delineating natural and human-induced hazards; (2) using the expression to leverage popularity; and (3) critiquing the expression "natural disaster." We also identified vulnerability themes that illustrate the context of "natural disaster" usage. The implications of continuing to use this expression, while explicitly researching human vulnerability, are wide-ranging, and we explore what this means for us and our peers. This study particularly aims to stimulate debate within the disaster studies research community and related fields as to whether the term "natural disaster" is really fit for purpose moving forward.
The Nature of “Natural Disasters”: Survivors' Explanations of Earthquake Damage
Alessandro Massazza, Chris R. Brewin, Helene Joffe
2019, 10(3): 293-305. doi: 10.1007/s13753-019-0223-z
The distinction between natural and human-made disasters is ingrained in everyday language. Disaster scientists have long been critical of this dichotomy. Nonetheless, virtually no attention has been paid to how disaster survivors conceptualize the causes of the disasters they experience. In this mixed-methods longitudinal study, 112 survivors of the 2016-2017 Central Italy earthquakes completed questionnaires 3 and 16 months following the earthquakes, with the aim of assessing attributions of blame for the earthquake damage. In-depth interviews were also conducted with 52 participants at the 3-month mark to explore representations of causation for the earthquake damage. The distinction between disasters caused by nature and disasters caused by humans was not supported by survivors of the earthquake. In the longitudinal surveys, building firms and the State were assigned as much blame as nature for the earthquake damage, at both 3 months and 16 months after the earthquakes. Corroborating this complexity, in the interviews, the causes of the earthquake damage, rather than being understood as purely natural, were perceived as a complex mosaic composed of political, technological, natural, and moral factors. This empirical work shows that disaster survivors combine both nature-based and human-based explanations of disasters, rather than subscribing to one or the other. These findings have practical implications for disaster risk reduction and response.
Challenges to Coordination: Understanding Intergovernmental Friction During Disasters
Daniel P. Aldrich
2019, 10(3): 306-316. doi: 10.1007/s13753-019-00225-1
While idealized crisis response involves smooth coordination between relevant actors, friction between levels of government and between the state and civil society in responding to catastrophe may be more common. This article builds a theory of cross-level friction during and after crisis by analyzing the conditions when discord is most likely. With a medium-N dataset (N=18) of disaster responses from, among other countries, Chile, Haiti, Japan, North America, the Philippines, and Somalia, I carry out quantitative and qualitative analysis of cases with a variety of levels of friction to investigate the conditions that lead to misalignment. Tobit regression, qualitative comparative analysis, and case studies that take into account levels of economic development, government structure, nongovernmental organization density, and levels of damage demonstrate that low levels of development, lower levels of economic costs from the crisis, and poor planning and logistical infrastructure correlate with a higher likelihood of friction between disaster response stakeholders. Although not definitive, these findings come with theoretical and practical implications as climate change makes extreme weather events and future disasters more likely and more powerful.
Early Warning Systems: Lost in Translation or Late by Definition? A FORIN Approach
Irasema Alcántara-Ayala, Anthony Oliver-Smith
2019, 10(3): 317-331. doi: 10.1007/s13753-019-00231-3
Early warning systems (EWSs) are widely considered to be one of the most important mechanisms to prevent disasters around the globe. But as disasters continue to affect countries where EWSs have already been implemented, the striking disaster consequences have led us to reflect on the focus, architecture, and function of the warning systems. Since the 2004 Indian Ocean tsunami there has been a rapid rise in the promotion and use of EWSs to minimize disaster losses and damage. However, few researchers have addressed the question of their acceptability as an adaptive measure to the existing exposure conditions. EWSs are far more linked to emergency response and humanitarian crises and accepted technological interventions as solutions than they are to explicitly advance integrated analysis, disaster risk reduction, and policy making. A major flaw of EWSs is that the term "early" has been essentially used in reference to the speed of hazard onset, founded on a physicalist perspective that has encouraged a considerable dependence on technology. In this article we address the need for a clear understanding of the root causes and risk drivers of disaster risk creation, as advanced in the FORIN (forensic investigation of disasters) approach, as a prerequisite for the development of more articulated EWSs that could contribute to disaster risk reduction through policy making and practice, based on integrated and transdisciplinary management, in the interest of sustainable development, and human welfare and well-being.
Persistent Precarity and the Disaster of Everyday Life: Homeless People's Experiences of Natural and Other Hazards
JC Gaillard, Vicky Walters, Megan Rickerby, Yu Shi
2019, 10(3): 332-342. doi: 10.1007/s13753-019-00228-y
Knowledge of how homeless people deal with natural hazards and disasters is sparse and there is a remarkable absence of homeless people in policies and practices for disaster risk reduction (DRR). This article aims at filling this gap by exploring the lives of homeless people in two New Zealand cities that are exposed to natural hazards. It shows that natural hazards are of marginal concern to homeless people in comparison to the everyday hazards that they experience and that make their everyday life a disaster in itself. The disaster of everyday life is created and compounded by homeless people's precarious lifeworlds. The article, nonetheless, shows that homeless people's vulnerability to natural hazards remains high as they lack power to control the processes that shape their everyday lives, to prepare for large-scale events, and to be represented in DRR policy. Therefore, the article ultimately argues that disaster policies require greater attention to be paid to the power structures that create persistent precarity and the ways in which this is experienced in everyday life.
Integrated Assessment of Natural Hazards, Including Climate Change's Influences, for Cultural Heritage Sites: The Case of the Historic Centre of Rethymno in Greece
Mohammad Ravankhah, Rosmarie de Wit, Athanasios V. Argyriou, Angelos Chliaoutakis, Maria João Revez, Joern Birkmann, Maja Žuvela-Aloise, Apostolos Sarris, Anastasia Tzigounaki, Kostas Giapitsoglou
2019, 10(3): 343-361. doi: 10.1007/s13753-019-00235-z
Within the framework of disaster risk management, this article proposes an interdisciplinary method for the analysis of multiple natural hazards, including climate change's influences, in the context of cultural heritage. A taxonomy of natural hazards applicable to cultural heritage was developed based on the existing theoretical and conceptual frameworks. Sudden-onset hazards, such as earthquakes and floods, and slow-onset hazards, such as wetting-drying cycles and biological contamination, were incorporated into the hazard assessment procedure. Future alteration of conditions due to climate change, such as change in heat waves' duration, was also taken into account. The proposed hazard assessment framework was applied to the case of the Historic Centre of Rethymno, a city on the northern coast of the island of Crete in Greece, to identify, analyze, and prioritize the hazards that have the potential to cause damage to the center's historic structures. The assessment procedure includes climate model projections, GIS spatial modeling and mapping, and finally a hazard analysis matrix to enable the sharing of a better understanding of multiple hazards with the stakeholders. The results can facilitate decision making by providing the vulnerability and risk analysis with the nature and spatial distribution of the significant hazards within the study area and its setting.
The Impact of Hurricane Strikes on Short-Term Local Economic Activity: Evidence from Nightlight Images in the Dominican Republic
Oscar A. Ishizawa, Juan José Miranda, Eric Strobl
2019, 10(3): 362-370. doi: 10.1007/s13753-019-00226-0
The Dominican Republic is highly exposed to adverse natural events that put the country at risk of losing hard-won economic, social, and environmental gains due to the impacts of disasters. This study used monthly nightlight composites in conjunction with a wind field model to econometrically estimate the impact of tropical cyclones on local economic activity in the Dominican Republic since 1992. It was found that the negative impact of storms lasts up to 15 months after a strike, with the largest effect observed after 9 months. Translating the reduction in nightlight intensity into monetary losses by relating it to quarterly gross domestic product (GDP) suggests that on average the storms reduced GDP by about USD 1.1 billion (4.5% of GDP 2000 and 1.5% of GDP 2016).
Impact of Economic Development Levels and Disaster Types on the Short-Term Macroeconomic Consequences of Natural Hazard-Induced Disasters in China
Rumei Tang, Jidong Wu, Mengqi Ye, Wenhui Liu
2019, 10(3): 371-385. doi: 10.1007/s13753-019-00234-0
The relationship between natural hazard-induced disasters and macroeconomic growth has been examined widely on global and national scales, but little research has been focused on the subnational level, especially in China. We examined the impacts of natural hazard-induced disasters on the regional growth in China based on subnational panel data for the period from 1990 to 2016. First, we used the number of people affected and the direct economic losses as the measures of the scale of disasters. Then, we used the direct damages of meteorological disasters and earthquakes as disaster measures separately to examine the impacts of different disaster types. Finally, we performed intraregional effects regressions to observe the spatial heterogeneity within the regions. The results show that the adverse short-term effects of disasters is most pronounced in the central region, while the direct damage of disasters is a positive stimulus of growth in the whole of China. However, this stimulus is observed in a lagged way and is reflected differently-meteorological disasters in central and eastern China and earthquakes in western China are related to regional growth. The results demonstrate that the short-term macroeconomic impacts of these disasters in the three geographical regions of China largely depend on regional economic development levels and the disaster types.
Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China
Guoli Zhang, Ming Wang, Kai Liu
2019, 10(3): 386-403. doi: 10.1007/s13753-019-00233-1
Forest fires have caused considerable losses to ecologies, societies, and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, we proposed a spatial prediction model for forest fire susceptibility using a CNN. Past forest fire locations in Yunnan Province, China, from 2002 to 2010, and a set of 14 forest fire influencing factors were mapped using a geographic information system. Oversampling was applied to eliminate the class imbalance, and proportional stratified sampling was used to construct the training/validation sample libraries. A CNN architecture that is suitable for the prediction of forest fire susceptibility was designed and hyperparameters were optimized to improve the prediction accuracy. Then, the test dataset was fed into the trained model to construct the spatial prediction map of forest fire susceptibility in Yunnan Province. Finally, the prediction performance of the proposed model was assessed using several statistical measures-Wilcoxon signed-rank test, receiver operating characteristic curve, and area under the curve (AUC). The results confirmed the higher accuracy of the proposed CNN model (AUC 0.86) than those of the random forests, support vector machine, multilayer perceptron neural network, and kernel logistic regression benchmark classifiers. The CNN has stronger fitting and classification abilities and can make full use of neighborhood information, which is a promising alternative for the spatial prediction of forest fire susceptibility. This research extends the application of CNN to the prediction of forest fire susceptibility.
Natural Hazards and Social Vulnerability of Place: The StrengthBased Approach Applied to Wollongong, Australia
Robert I. Ogie, Biswajeet Pradhan
2019, 10(3): 404-420. doi: 10.1007/s13753-019-0224-y
Natural hazards pose significant threats to different communities and various places around the world. Failing to identify and support the most vulnerable communities is a recipe for disaster. Many studies have proposed social vulnerability indices for measuring both the sensitivity of a population to natural hazards and its ability to respond and recover from them. Existing techniques, however, have not accounted for the unique strengths that exist within different communities to help minimize disaster loss. This study proposes a more balanced approach referred to as the strength-based social vulnerability index (SSVI). The proposed SSVI technique, which is built on sound sociopsychological theories of how people act during disasters and emergencies, is applied to assess comparatively the social vulnerability of different suburbs in the Wollongong area of New South Wales, Australia. The results highlight suburbs that are highly vulnerable, and demonstrates the usefulness of the technique in improving understanding of hotspots where limited resources should be judiciously allocated to help communities improve preparedness, response, and recovery from natural hazards.