2022 Vol. 13, No. 5

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Sendai Framework’s Global Targets A and B: Opinions from the Global Platform for Disaster Risk Reduction’s Ignite Stage 2019
Nibedita S. Ray-Bennett, Krishna Clarke, Daniel Mendez
2022, 13(5): 651-663. doi: 10.1007/s13753-022-00432-3
The Sendai Framework for Disaster Risk Reduction 2015-2030 set seven global targets of which the first two targets are to reduce disaster deaths (target A) and diminish the number of affected people globally (target B) by 2030. To realize these targets, the United Nations General Assembly's Expert Working Group provided indicators to measure progress as well as terminologies for these targets in 2017. Research around these targets is nascent. This article contributes to the understanding of the targets by exploring:(1) what are the conditions that may hinder achieving targets, as well as those that may accelerate their achievement at the national and local levels; and (2) which types of organizations should lead a country's effort to reduce disaster deaths? These questions were answered by opinion survey research carried out at the Sixth Session of the Global Platform for Disaster Risk Reduction. The participants identified disaster risk reduction efforts, early warning systems, awareness, finance and investment (among others) as the important facilitating factors to achieve targets A and B. Minimal investment in human security, lack of response and coordination, uncertainty of climate change, poor information, lack of campaigns and low budget allocation (among others) are considered as the important hindering factors for these targets by the participants. The findings also suggest that the facilitating and hindering variables of targets A and B are interconnected with global target E (disaster risk governance and capacity building). The majority of the participants thought that it is the national government who should lead in a country's effort to reduce disaster deaths. Based on these findings, a few recommendations have been made to improve policy and practice related to the indicators as well as to reimagine theories so that targets A and B can be realized in alignment with target E at the national and local levels by 2030.
Perceptions About Climate Change in the Brazilian Civil Defense Sector
Victor Marchezini, Luciana Resende Londe, Eloisa Beling Loose, Silvia Midori Saito, José A. Marengo
2022, 13(5): 664-674. doi: 10.1007/s13753-022-00444-z
Few studies have analyzed climate change perceptions in the disaster risk management sector. This research aimed to understand how civil defense experts are dealing with the climate change topic:what they learn and think about. An online survey was conducted between October and December 2021 with 1,063 participants from civil defense agencies in Brazil. The findings indicate:(1) most (80.6%) civil defense officers completely agreed that climate change will cause additional challenges to disaster risk management, while 10.1% stated that they are prepared to deal with it; (2) one quarter of the respondents (26.3%) completely agreed that they easily understand the information about climate change, but trust in the sources of information is a challenge-52.4% completely agreed and 40.5% partially agreed with information provided by scientists, but the levels of trust were reduced when referring to governments and press; and (3) about 30% of the respondents thought that civil defense work is associated with the Sustainable Development Goals (SDGs), despite SDGs 11 and 13 being related to disasters and climate change. The identification of civil defense' perceptions on climate change is an important step in seeking pathways for increasing capacity building to achieve disaster risk reduction and climate change adaptation.
Correction: Perceptions About Climate Change in the Brazilian Civil Defense Sector
Victor Marchezini, Luciana Resende Londe, Eloisa Beling Loose, Silvia Midori Saito, José A. Marengo
2022, 13(5): 675-675. doi: 10.1007/s13753-022-00448-9
Is Being Funny a Useful Policy? How Local Governments’ Humorous Crisis Response Strategies and Crisis Responsibilities Influence Trust, Emotions, and Behavioral Intentions
Janna Hämpke, Stefan Röseler, Meinald T. Thielsch
2022, 13(5): 676-690. doi: 10.1007/s13753-022-00436-z
This study is the first to investigate how a local government's humorously framed response strategy on social media to a low-severity crisis influences people's trust in the local government and their crisis-related behavioral intentions, specifically when considering the government's responsibility for the crisis. Based on the situational crisis communication theory, we examined the mediating role of experienced positive or negative affect on people's responses to a local government's crisis communication strategy. Further, we exploratorily examined the predictive power and moderating role of demographics, sense of humor, disposition to trust, and the respective crisis scenarios. A total of 517 people participated in an online experiment in which they were confronted with three randomly presented fictive crisis scenarios where the local government's crisis responsibility (high versus low) and the framing of their crisis response strategy (in form of humorous versus rational Twitter posts) were systematically varied between subjects. First, the results mostly corroborate earlier findings about the degree of crisis responsibility (that is, when a government's crisis responsibility is high, people have less trust and behavioral intentions) and about the mediating role of experienced affect. Second, we found that humorously framed strategies negatively influence trust and positive affect (but not behavioral intentions). In contrast to earlier findings, the crisis responsibility × framing interaction was not significant. Altogether, the results advise against using humor in crisis communications on social media, even in low-severity crisis. Exploratory analyses indicate that further investigations should focus on specific crisis characteristics and potential moderators.
The Effect of Natural Hazard Damage on Manufacturing Value Added and the Impact of Spatiotemporal Data Variations on the Results
Douglas Thomas, Jennifer Helgeson
2022, 13(5): 691-704. doi: 10.1007/s13753-022-00438-x
This study examined the effect of natural hazards on manufacturing industry value added and the sensitivity of the results from changes to spatiotemporal resolution of the data. We measured the negative effects of hazards, rather than the net effect. Three models were developed with varying spatiotemporal units for the continental United States:annual/county units; annual/state units; and quarterly/state units. Three simulations were run using each model to estimate the negative effect of damage from all natural hazards on value added across spatiotemporal scales. Finally, an investment analysis was conducted to examine the return from public investments in hazard resilience. The results do not demonstrate that, locally, natural hazards reduce value added. However, the evidence suggests that natural hazards in the upstream supply chain have statistically significant impact when modeled at the annual/county scale and at the quarterly/state scale. Neither local nor supply chain hazards have a statistically significant effect when modeled at the annual/state scale, suggesting that broader spatiotemporal units may obscure the true downstream effects of natural hazards. The investment analysis, utilizing model results, suggests that an investment of USD 100 billion or less is economical if it results in a reduction in losses of 10% or more.
Understanding Farmers’ Preferences Towards Insurance Schemes that Promote Biosecurity Best Management Practices
Rosa Mato-Amboage, Julia Touza, Mario Soliño
2022, 13(5): 705-715. doi: 10.1007/s13753-022-00435-0
Plant pest and disease outbreaks, which occur with increasing frequency and intensity, cause catastrophic losses and threaten food security in many areas around the world. These impacts are expected to be exacerbated by climate change. Tackling this challenge requires mechanisms that ensure the financial security of farmers while incentivizing private biosecurity efforts to prevent future outbreaks. This study explored crop producers' preferences for a subsidized insurance scheme as an instrument to manage novel biotic risks. Specifically, we developed a choice experiment to evaluate Spanish growers' willingness to pay for a crop insurance product that promotes compliance with best biosecurity management practices. Our results show that while growers are willing to pay more for high coverage products that increase the resilience of crops to potential catastrophic outbreaks, there is neither a strong demand nor widespread availability of such tools. Farmers required reductions in premiums before undertaking risk prevention measures; they are more willing to pay for schemes that link their eligibility to access to ad hoc funds in the eventuality of a catastrophic outbreak than they are to purchase insurance. Our findings also suggest that Spanish growers prefer expanding the eligible risks covered by insurance and envisage a role for insurance in offering biosecurity protection.
Institutional Capacity and the Roles of Key Actors in Fire Disaster Risk Reduction: The Case of Ibadan, Nigeria
Olusegun Joseph Falola, Samuel Babatunde Agbola
2022, 13(5): 716-728. doi: 10.1007/s13753-022-00440-3
Inefficient and ineffective fire management practices are common to most urban areas of developing countries. Nigerian cities are typical examples of high vulnerability and low preparedness level for fire disaster. This study examined the institutional framework for fire disaster risk reduction (FDRR) and explored the roles of key actors in fire disaster preparedness in Ibadan, a large traditional city in Nigeria. The study was anchored on the concept of urban governance. A case study research design was adopted using primary and secondary data. Primary data were obtained through field observation aided by a structured checklist and key informant interview. Interviews were conducted on key officials of the major organs for FDRR-Oyo State Fire Service (OSFS) and Oyo State Emergency Management Agency (OYSEMA). The study identified a disjointed and fragmented approach to fire management. Matters relating to fire risk reduction and disaster recovery were domiciled under the OYSEMA, while emergency response to fire disasters was the prerogative of the OSFS. The results show that only five out of 11 local government areas had public fire stations; only three fire stations had an on-site water supply; three fire stations lacked firefighting vehicles; and distribution of fire stations and facilities was uneven. Two fire stations responded to 80% of all fire cases in 12 years. The study concluded that the institutional structure and resources for fire risk reduction was more empowered to respond to fire disaster, rather than facilitating preparedness capacity to reduce disaster risk.
Disaster Impacts Surveillance from Social Media with Topic Modeling and Feature Extraction: Case of Hurricane Harvey
Volodymyr V. Mihunov, Navid H. Jafari, Kejin Wang, Nina S. N. Lam, Dylan Govender
2022, 13(5): 729-742. doi: 10.1007/s13753-022-00442-1
Twitter can supply useful information on infrastructure impacts to the emergency managers during major disasters, but it is time consuming to filter through many irrelevant tweets. Previous studies have identified the types of messages that can be found on social media during disasters, but few solutions have been proposed to efficiently extract useful ones. We present a framework that can be applied in a timely manner to provide disaster impact information sourced from social media. The framework is tested on a well-studied and data-rich case of Hurricane Harvey. The procedures consist of filtering the raw Twitter data based on keywords, location, and tweet attributes, and then applying the latent Dirichlet allocation (LDA) to separate the tweets from the disaster affected area into categories (topics) useful to emergency managers. The LDA revealed that out of 24 topics found in the data, nine were directly related to disaster impacts-for example, outages, closures, flooded roads, and damaged infrastructure. Features such as frequent hashtags, mentions, URLs, and useful images were then extracted and analyzed. The relevant tweets, along with useful images, were correlated at the county level with flood depth, distributed disaster aid (damage), and population density. Significant correlations were found between the nine relevant topics and population density but not flood depth and damage, suggesting that more research into the suitability of social media data for disaster impacts modeling is needed. The results from this study provide baseline information for such efforts in the future.
Estimating Ground Snow Load Based on Ground Snow Depth and Climatological Elements for Snow Hazard Assessment in Northeastern China
Huamei Mo, Guolong Zhang, Qingwen Zhang, H. P. Hong, Feng Fan
2022, 13(5): 743-757. doi: 10.1007/s13753-022-00443-0
Extreme snow loads can collapse roofs. This load is calculated based on the ground snow load (that is, the snow water equivalent on the ground). However, snow water equivalent (SWE) measurements are unavailable for most sites, while the ground snow depth is frequently measured and recorded. A new simple practical algorithm was proposed in this study to evaluate the SWE by utilizing ground snow depth, precipitation data, wind speed, and air temperature. For the evaluation, the precipitation was classified as snowfall or rainfall according to the air temperature, the snowfall or rainfall was then corrected for measurement error that is mainly caused by wind-induced undercatch, and the effect of snow water loss was considered. The developed algorithm was applied and validated using data from 57 meteorological stations located in the northeastern region of China. The annual maximum SWE obtained based on the proposed algorithm was compared with that obtained from the actual SWE measurements. The return period values of the annual maximum ground snow load were estimated and compared to those obtained according to the procedure suggested by the Chinese structural design code. The comparison indicated that the use of the proposed algorithm leads to a good estimated SWE or ground snow load. Its use allowed the estimation of the ground snow load for sites without SWE measurement and facilitated snow hazard mapping.
Induced Earthquake Hazard by Geothermal Power Plants: Statistical Evaluation and Probabilistic Modeling
Ali Khansefid, Seyed Mahmoudreza Yadollahi, Gerhard Müller, Francesca Taddei
2022, 13(5): 758-777. doi: 10.1007/s13753-022-00441-2
This study statistically evaluated the characteristics of induced earthquakes by geothermal power plants (GPPs) and generated a probabilistic model for simulating stochastic seismic events. Four well-known power plant zones were selected worldwide from the United States, Germany, France, and New Zealand. The operational condition information, as well as the corresponding earthquake catalogs recorded in the vicinity of GPPs, were gathered from their commencement date. The statistical properties of events were studied elaborately. By using this proposed database, a probabilistic model was developed capable of generating the number of induced seismic events per month, their magnitude, focal depth, and distance from the epicenter to the power plant, randomly. All of these parameters are simulated as a function of power plant injection rate. Generally speaking, the model, introduced in this study, is a tool for engineers and scientists interested in the seismic risk assessment of built environments prone to induced seismicity produced by GPPs operation.
Multiperiod Equitable and Efficient Allocation Strategy of Emergency Resources Under Uncertainty
Yanyan Wang, Baiqing Sun
2022, 13(5): 778-792. doi: 10.1007/s13753-022-00437-y
Equitable and efficient allocation of emergency resources is critical to ensure the success of relief efforts. The challenge comes largely from two aspects:the resources available for allocation are usually limited in quantity, especially in the early period of emergency response; and a large amount of uncertain information in the relief process affects the decision making of resource allocation. A multiperiod allocation model of emergency resources that takes into account both efficiency and equity based on uncertain disaster information is proposed. Interval number and triangular fuzzy number are introduced to describe the different sources of uncertainty (for example, demand, transportation time, and maximum transport amount), and the loss caused by unmet demand is used to quantify equity. Then, the deterministic transformation method of uncertain parameters is designed and the linear weighted sum method is applied to solve the proposed model. Finally, a computational case based on the 2017 Jiuzhaigou earthquake in Sichuan Province, China was conducted to validate the proposed model. The results show that the proposed model is feasible in the multiperiod allocation of emergency resources among multi-disaster sites, and the findings can help emergency managers to allocate emergency resources more scientifically, equitably, and effectively under uncertainty.
Network Structure Optimization Method for Urban Drainage Systems Considering Pipeline Redundancies
Jiahui Lu, Jiahong Liu, Yingdong Yu, Chuang Liu, Xin Su
2022, 13(5): 793-809. doi: 10.1007/s13753-022-00445-y
Redundancy is an important attribute of a resilient urban drainage system. While there is a lack of knowledge on where to increase redundancy and its contribution to resilience, this study developed a framework for the optimal network structure of urban drainage systems that considers pipeline redundancies. Graph theory and adaptive genetic algorithms were used to obtain the initial layout and design of the urban drainage system. The introduction of additional water paths (in loop)/redundancies is suggested by the results of complex network analysis to increase resilience. The drainage performances of the urban drainage system with pipeline redundancies, and without redundancies, were compared. The proposed method was applied to the study area in Dongying City, Shandong Province, China. The results show that the total overflow volume of the urban drainage system with pipeline redundancies under rainfall exceeding the design standard (5 years) is reduced by 20-30%, which is substantially better than the network without pipeline redundancies.
DARTS—Drone and Artificial Intelligence Reconsolidated Technological Solution for Increasing the Oil and Gas Pipeline Resilience
Premkumar Ravishankar, Seokyon Hwang, Jing Zhang, Ibrahim X. Khalilullah, Berna Eren-Tokgoz
2022, 13(5): 810-821. doi: 10.1007/s13753-022-00439-w
The need for safe operation and effective maintenance of pipelines grows as oil and gas demand rises. Thereby, it is increasingly imperative to monitor and inspect the pipeline system, detect causes contributing to developing pipeline damage, and perform preventive maintenance in a timely manner. Currently, pipeline inspection is performed at pre-determined intervals of several months, which is not sufficiently robust in terms of timeliness. This research proposes a drone and artificial intelligence reconsolidated technological solution (DARTS) by integrating drone technology and deep learning technique. This solution is aimed to detect the targeted potential root problems-pipes out of alignment and deterioration of pipe support system-that can cause critical pipeline failures and predict the progress of the detected problems by collecting and analyzing image data periodically. The test results show that DARTS can be effectively used to support decision making for preventive pipeline maintenance to increase pipeline system safety and resilience.
On the Meaning of Impact in Disaster Risk Reduction
David E. Alexander
2022, 13(5): 822-827. doi: 10.1007/s13753-022-00447-w
This article offers a discussion of the meaning, assessment, and measurement of impact in disaster risk reduction. It begins with a historical perspective on the impact of learned work in times when orthodoxy posed severe limits on the impact of new thinking. Regarding the modern age, the article explains why impact is considered important and how it might be recognized when it occurs, including a tentative classification of types of impact. The question of whether impact can truly be measured remains pending, as the answer is diffuse and dependent on many different circumstances. Further sections consider the relationship between impact and mainstreaming and the question of whether a piece of work should be regarded as having impact if its effects are negative rather than positive. Next, impact is considered in terms of whom it benefits. Given the large number of possible reservations about the concept, the question is raised as to whether too much emphasis is given to the impact of research and scholarship. Finally, some suggestions are offered regarding how to obtain a better indication of what the impact of an academic study actually is.