International Journal of Disaster Risk Science

2023 Vol. 14, No. 3

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Integrated Disaster Risk Management (IDRM): Elements to Advance Its Study and Assessment

Vicente Sandoval, Martin Voss, Verena Flörchinger, Stephan Lorenz, Parisa Jafari

2023, 14(3): 343-356. doi: 10.1007/s13753-023-00490-1

Abstract(962) PDF

Abstract:
This study analyzed the international key literature on integrated disaster risk management (IDRM), considering it a dynamic sociocultural process subjected to the historical process of social formation, offering a closer look at the concept while exploring conceptual elements and ideas to advance IDRM in both national and international contexts. Methodologically, the study adopted a literature review strategy, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, combined with qualitative content analysis. This article examines the history of IDRM, discusses current challenges for implementation, looks at some experiences, and proposes avenues for further research. Some findings point out the lack of an overarching IDRM approach, which is characterized by a rather disperse set of ideas and experiences concerning what IDRM is and how it should be operationalized, thereby revealing the need for a more comprehensive theory and methodologies to further advance it. Other findings highlight that IDRM encompasses different kinds and levels of “integrations” that go from internal (that is, disaster risk reduction and management domains) to external (that is, all societal processes such as sustainable development), including temporal and spatial integrations. Hence, we are talking about a multidimensional integration of disaster risk management. In this regard, the article proposes four dimensions for integration: sectoral, spatial/hierarchical, temporal, and externally with other cross-cutting societal challenges, especially climate change and sustainable development. These dimensions cover 29 ideas for indicators or “proto-indicators” to guide the discussion, exploration, and analysis of IDRM in specific contexts.

The Application of Model-Based Systems Engineering to Rural Healthcare System Disaster Planning: A Scoping Review

Thomas A. Berg, Kelsi N. Marino, Kristina W. Kintziger

2023, 14(3): 357-368. doi: 10.1007/s13753-023-00492-z

Abstract(136) PDF

Abstract:
Disasters and other emergency events have complex effects on human systems, particularly if the events are severe or prolonged. When these types of events happen in rural communities, the resources of the local public health, healthcare, and emergency response organizations can be quickly depleted or overwhelmed. Planning for emergencies can help to mitigate their impact. Model-based systems engineering (MBSE) methods, including computer simulations, can provide insight on how best to prepare for these events and to explore the effects of varying approaches and resource utilization. To best apply these methods for improving disaster management in rural settings, a synthesis of the current body of evidence in this field is needed. The objective of this scoping review was to provide a descriptive overview of the application of computer simulation based on MBSE approaches to disaster preparedness and response for rural healthcare systems. Six studies met inclusion criteria, and varied in terms of MBSE method used, healthcare setting, and disaster type and context considered. We identified a gap in the research regarding the application of MBSE approaches to support rural healthcare disaster preparedness planning efforts. Model-based systems engineering and systems thinking, therefore, represent novel methods for developing tools and computational simulations that could assist rural communities better prepare for disasters.

Human–Animal Interactions in Disaster Settings: A Systematic Review

Haorui Wu, Lindsay K. Heyland, Mandy Yung, Maryam Schneider

2023, 14(3): 369-381. doi: 10.1007/s13753-023-00496-9

Abstract(219) PDF

Abstract:
This systematic review aimed to assess the current knowledge of human–animal interactions (HAIs) in disaster settings and identify areas for future research. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses search was conducted on three multidisciplinary databases, identifying English-language journal articles published between January 2000 and February 2022 that explored the benefits of and challenges associated with HAI in disasters and emergencies. The review analyzed 94 articles using both quantitative and qualitative methods. The review found a paucity of universal terminology to describe the bidirectional relationship between humans and animals during disasters and a failure to include all animal types in every stage of disaster and emergency management. Additionally, research predominantly focused on the health and well-being benefits of HAI for humans rather than animals. Efforts to promote social and environmental justice for humans and their co-inhabitants should support the welfare of both humans and animals in disaster settings. Four recommendations were developed based on these findings to increase the inclusion of HAI in research, policy, and practice. Limitations of the review included the exclusion of pre-2000 articles and all grey literature, limited research examining different combinations of animal and disaster types, and limited research outside of North America.

Mainstreaming Decentralization and Collaboration in Disaster Risk Management: Insights from Coastal Bangladesh

Mohammad Abdul Quader, Amanat Ullah Khan, Md. Abdul Malak, Matthieu Kervyn

2023, 14(3): 382-397. doi: 10.1007/s13753-023-00495-w

Abstract(214) PDF

Abstract:
Bangladesh is renowned in disaster risk reduction (DRR) for active involvement of community people and local disaster management institutions in DRR activities. Our study aimed to describe the disaster risk management (DRM) institutions and assess their functioning in six coastal unions across the three coastal zones of Bangladesh. Both qualitative and quantitative research approaches were used. The study focused on two key local institutions—the Union Disaster Management Committees (UDMCs) and the Cyclone Preparedness Program (CPP)—functioning at the union level in DRM. Such institutions have both horizontal and vertical collaborations with other institutions. However, we argue that the UDMCs’ external dependencies in their functioning indicate their limited financial and administrative autonomy, which is a barrier to successfully institutionalizing disaster management. The results show that the CPP is the most successful program, markedly increasing the trust of the people in warning dissemination and evacuation efforts in the event of a cyclone. Although the adoption of decentralized risk management systems has resulted in significant progress in increased rate of evacuation and reduced death rate and damage, lack of funding and equipment, limited coordination between institutions, lack of skilled and knowledgeable workforce, and inappropriate power structures may reduce the effectiveness of DRR activities prior to, during, and following disasters.

Financing Disaster Risk Reduction: Exploring the Opportunities, Challenges, and Threats Within the Southern African Development Community Region

Christo Coetzee, Sizwile Khoza, Livhuwani D. Nemakonde, Lesego B. Shoroma, Gideon W. Wentink, Maynard Nyirenda, Steven Chikuse, Tchaka Kamanga, Kgosietsile Maripe, Morenaogaufi J. Rankopo, Lengwe-Katembula Mwansa, Dewald Van Niekerk

2023, 14(3): 398-412. doi: 10.1007/s13753-023-00499-6

Abstract(91) PDF

Abstract:
The Southern African Development Community (SADC) region, a regional economic body comprised of 16 member states, is one of our planet’s most vulnerable regions to natural hazards, and has a complex disaster risk profile. The region has sustained several disasters over the past decades. These events include annual floods in 2004–2019 and extreme droughts (1990–1993); other climate-induced disasters, such as cyclones, also have had devastating impacts, particularly on the Indian Ocean island states and east coast countries. To reduce the risk and impacts of disasters, governments must invest in disaster risk reduction (DRR). However, interventions aimed at reducing social and economic vulnerability and investing in long-term mitigation activities are often few, poorly funded, and insignificant in comparison with money spent on humanitarian assistance, disaster relief, and post-disaster reconstruction. This study investigated whether DRR is adequately funded within SADC member states in light of the high stakes in human life, infrastructure, and economic losses and the potential savings involved. The study applied a qualitative research design with data collected through semistructured interviews and focus group discussions. Respondents were selected purposefully and through snowball sampling with a total of 67 respondents from Botswana, Eswatini, Namibia, South Africa, and Zimbabwe participating in the study. The study findings reveal that DRR is inadequately funded in all the member states consulted in comparison to funding allocated to disaster response. In light of the underfunding experienced by DRR activities, this study provides a platform for lobbying and advocacy for adequate funding for DRR.

A Model of the Sea–Land Transition of the Mean Wind Profile in the Tropical Cyclone Boundary Layer Considering Climate Changes

Jiayao Wang, Tim K. T. Tse, Sunwei Li, Jimmy C. H. Fung

2023, 14(3): 413-427. doi: 10.1007/s13753-023-00488-9

Abstract(145) PDF

Abstract:
The tropical cyclone boundary layer (TCBL) connecting the underlying terrain and the upper atmosphere plays a crucial role in the overall dynamics of a tropical cyclone system. When tropical cyclones approach the coastline, the wind field inside the TCBL makes a sea–land transition to impact both onshore and offshore structures. So better understanding of the wind field inside the TCBL in the sea–land transition zone is of great importance. To this end, a semiempirical model that integrates the sea–land transition model from the Engineering Sciences Data Unit (ESDU), Huang’s refined TCBL wind field model, and the climate change scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is used to investigate the influence of climate changes on the sea–land transition of the TCBL wind flow in Hong Kong. More specifically, such a semiempirical method is employed in a series of Monte-Carlo simulations to predict the wind profiles inside the TCBL across the coastline of Hong Kong under the impact of future climate changes. The wind profiles calculated based on the Monte-Carlo simulation results reveal that, under the influences of the most severe climate change scenario, slightly higher and significantly lower wind speeds are found at altitudes above and below 400 m, respectively, compared to the wind speeds recommended in the Hong Kong Wind Code of Practice. Such findings imply that the wind profile model currently adopted by the Hong Kong authorities in assessing the safety of low- to high-rise buildings may be unnecessarily over-conservative under the influence of climate change. On the other hand, the coded wind loads on super-tall buildings slightly underestimate the typhoon impacts under the severe climate change conditions anticipated for coastal southern China.

A Rapid Estimation Method for Post-earthquake Building Losses

Dengke Zhao, Zifa Wang, Jianming Wang, Dongliang Wei, Yang Zhou, Zhaoyan Li

2023, 14(3): 428-439. doi: 10.1007/s13753-023-00491-0

Abstract(304) PDF

Abstract:
Rapid estimation of post-earthquake building damage and loss is very important in urgent response efforts. The current approach leaves much room for improvement in estimating ground motion and correctly incorporating the uncertainty and spatial correlation of the loss. This study proposed a new approach for rapidly estimating post-earthquake building loss with reasonable accuracy. The proposed method interpolates ground motion based on the observed ground motion using the Ground Motion Prediction Equation (GMPE) as the weight. It samples the building seismic loss quantile considering the spatial loss correlation that is expressed by Gaussian copula, and kriging is applied to reduce the dimension of direct sampling for estimation speed. The proposed approach was validated using three historical earthquake events in Japan with actual loss reports, and was then applied to predict the building loss amount for the March 2022 Fukushima Mw7.3 earthquake. The proposed method has high potential in future emergency efforts such as search, rescue, and evacuation planning.

Risk of Flash Floods in Urban and Rural Municipalities Triggered by Intense Precipitation in Wielkopolska of Poland

Iwona Pińskwar, Adam Choryński, Dariusz Graczyk

2023, 14(3): 440-457. doi: 10.1007/s13753-023-00493-y

Abstract(306) PDF

Abstract:
This research analyzed interventions of State Fire Service (SFS) units in the Wielkopolska region of Poland that were triggered by extreme precipitation for the period 2010–2021. Our results demonstrated that the most populated and urbanized towns in the Wielkopolska (Greater Poland, west of Warsaw) region are at the most risk in the event of extreme precipitation occurrence as measured by the total number of interventions made by the SFS. The number of SFS unit interventions in towns, standardized to 10,000 inhabitants, indicates that the highest proportional volume of interventions also occurred in smaller towns. In the rural municipalities the number of SFS unit interventions increases along with higher population density and proportion of infrastructure areas. As analyzed in this study, the 12 years from 2010 to 2021 were characterized by a higher number of days with heavy precipitation, for example, 20, 30, 40, and 50 mm, in comparison to the previous periods 1961–2010 and 1981–2010. Intervention databases collected by emergency services are a valuable source of information for hazard mapping. Based on those and other available data, a statistical model was created and factors influencing the local and regional occurrence of interventions were determined. Increasing suburbanization, the rising proportion of impermeable surfaces, and the impact of climate change are of considerable importance in urban flood risk. It is necessary to help municipalities develop abilities to absorb larger amounts of rainwater.

Effects of Land Use Changes Across Different Urbanization Periods on Summer Rainfall in the Pearl River Delta Core Area

Zhijun Yao, Guoru Huang

2023, 14(3): 458-474. doi: 10.1007/s13753-023-00497-8

Abstract(125) PDF

Abstract:
The Pearl River Delta (PRD) is one of the three urban agglomerations in China that have experienced rapid development. For this study, a core area of the PRD was identified, comprising the highly urbanized areas of Guangzhou, Foshan, Zhongshan, Zhuhai, Shenzhen, and Dongguan Cities. The expansion of these urban areas was tracked across three time periods—the year population urbanization rate exceeded 70% (2000), 18 years before (1982), and 18 years after (2018). This study used the Weather Research and Forecasting (WRF) model to explore summer rainfall changes across different urbanization periods in the PRD core area. The results show that urban land expansion mainly occurred in the post urbanization period. Rainfall changes across different urbanization periods were roughly consistent with previously observed spatial and temporal changes accompanying urban expansion in the PRD core area. Extreme rainfall mainly increased in the post urbanization period, shifting rainstorm center towards the PRD core area. Further causal analysis revealed that land use changes affected rainfall by altering thermodynamics and water vapor transfer. The urban expansion changed the surface energy balance, resulting in increased surface heating and heat island effects. The heat island effects thickened the planetary boundary layer and increased vertical wind speeds, which initiated dry island effects, thereby causing more water vapor transportation to the atmosphere. Consequently, rainstorms and extreme rainfall events have become concentrated in urban areas.

An Urban Road Risk Assessment Framework Based on Convolutional Neural Networks

Juncai Jiang, Fei Wang, Yizhao Wang, Wenyu Jiang, Yuming Qiao, Wenfeng Bai, Xinxin Zheng

2023, 14(3): 475-487. doi: 10.1007/s13753-023-00498-7

Abstract(198) PDF

Abstract:
In contemporary cities, road collapse is one of the most common disasters. This study proposed a framework for assessing the risk of urban road collapse. The framework first established a risk indicator system that combined environmental and anthropogenic factors, such as soil type, pipeline, and construction, as well as other indicators. Second, an oversampling technique was used to create the dataset. The framework then constructed and trained a convolutional neural network (CNN)-based model for risk assessment. The experimental results show that the CNN model (accuracy: 0.97, average recall: 0.91) outperformed other models. The indicator contribution analysis revealed that the distance between the road and the construction site (contribution: 0.132) and the size of the construction (contribution: 0.144) are the most significant factors contributing to road collapse. According to the natural breaks, a road collapse risk map of Foshan City, Guangdong Province, was created, and the risk level was divided into five categories. Nearly 3% of the roads in the study area are at very high risk, and 6% are at high risk levels, with the high risk roads concentrated in the east and southeast. The risk map produced by this study can be utilized by local authorities and policymakers to help maintain road safety.

Assessing the Regional Economic Ripple Effect of Flood Disasters Based on a Spatial Computable General Equilibrium Model Considering Traffic Disruptions

Lijiao Yang, Xinge Wang, Xinyu Jiang, Hirokazu Tatano

2023, 14(3): 488-505. doi: 10.1007/s13753-023-00500-2

Abstract(86) PDF

Abstract:
With growing regional economic integration, transportation systems have become critical to regional development and economic vitality but vulnerable to disasters. However, the regional economic ripple effect of a disaster is difficult to quantify accurately, especially considering the cumulated influence of traffic disruptions. This study explored integrating transportation system analysis with economic modeling to capture the regional economic ripple effect. A state-of-the-art spatial computable general equilibrium model is leveraged to simulate the operation of the economic system, and the marginal rate of transport cost is introduced to reflect traffic network damage post-disaster. The model is applied to the 50-year return period flood in 2020 in Hubei Province, China. The results show the following. First, when traffic disruption costs are considered, the total output loss of non-affected areas is 1.81 times than before, and non-negligible losses reach relatively remote zones of the country, such as the Northwest Comprehensive Economic Zone (36% of total ripple effects). Second, traffic disruptions have a significant hindering effect on regional trade activities, especially in the regional intermediate input—about three times more than before. The industries most sensitive to traffic disruptions were transportation, storage, and postal service (5 times), and processing and assembly manufacturing (4.4 times). Third, the longer the distance, the stronger traffic disruptions’ impact on interregional intermediate inputs. Thus, increasing investment in transportation infrastructure significantly contributes to mitigating disaster ripple effects and accelerating the process of industrial recovery in affected areas.