2021 Vol. 12, No. 1

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Perceived Adverse Effects of Separating Government Institutions for Disaster Risk Reduction and Climate Change Adaptation Within the Southern African Development Community Member States
Livhuwani D. Nemakonde, Dewald Van Niekerk, Per Becker, Sizwile Khoza
2021, 12(1): 1-12. doi: 10.1007/s13753-020-00303-9
Integration of disaster risk reduction (DRR) and climate change adaptation (CCA) is widely recognized as a solution for reducing the risk and impacts of disasters. However, successful integration seems elusive, and the two goals continue to function in isolation and in parallel. This article provides empirical insights into the perceived effects of separating government institutions for DRR and CCA within the Southern African Development Community member states. A mixed method research design was applied to the study. A total of 40 respondents from Botswana, Eswatini (until April 2018 Swaziland), Madagascar, Malawi, Namibia, South Africa, Tanzania, Zambia, and Zimbabwe participated in face-to-face interviews or an online survey. Five major effects of separating the organizations for DRR and CCA that impede efforts to reduce disaster risk coherently were identified: duplication of services, polarization of interventions, incoherent policies, competition for the same resources, and territorial contests. Given the continued fragmentation of institutions for DRR and CCA, highlighting these effects is important to emphasize the need for integrated approaches towards the reduction of disaster risk.
Social Vulnerability Factors and Reported Post-Disaster Needs in the Aftermath of Hurricane Florence
Julia Crowley
2021, 12(1): 13-23. doi: 10.1007/s13753-020-00315-5
This research examines the relationship between social vulnerability factors and reported needs following Hurricane Florence. Weighted least squares regression models were used to identify predictor variables for valid registrations that reported needs pertaining to emergencies, food, and shelter. Data consisted of zip codes in North Carolina and South Carolina that received individual assistance for Hurricane Florence (N = 406). The results suggest that when controlling for event-specific factors and flood mitigation factors, the proportions of the population that is female, the population over 65, the population aged 5 and under, the population older than 5 years not speaking English, and the minority population were all predictors of the per capita reported emergency needs. When controlling for the same variables, the proportions of the population over the age of 25 with a Bachelor’s degree, the female population, the population aged 5 and under, the population above 5 years old that does not speak English, and the minority population were all predictors of the per capita reported food needs. With the same variables controlled for, three variables—the proportions of the population over 65, the population aged 5 and under, and the non-Englishspeaking population above 5 years of age—were all predictors of the per capita reported shelter needs. The results suggest that more attention should be given to these vulnerable populations in the pre-disaster planning process.
An Inquiry into Success Factors for Post-disaster Housing Reconstruction Projects: A Case of Kerala, South India
Shyni Anilkumar, Haimanti Banerji
2021, 12(1): 24-39. doi: 10.1007/s13753-020-00309-3
The 2004 Indian Ocean Tsunami triggered significant destruction to housing and related infrastructures across various coastal districts of south India. Research shows that tsunami reconstruction projects in Kerala experienced different degrees of success and failure. On this background, this study explored factors that contributed to the successful implementation of tsunami housing projects in Kerala by (1) consolidating various critical success factors (CSFs) for post-disaster reconstruction (PDR) projects under “project management success traits” through content analysis of existing literature; (2) deriving a conceptual model that envisages project success in PDR contexts; and (3) assessing the impacts of those success traits on tsunami housing projects using confirmatory factor analysis. Necessary data were gathered through a survey of various stakeholders involved in tsunami reconstruction projects in Kerala using structured questionnaires. The research revealed that PDR project success is attributed to critical dimensions of project management such as institutional mechanisms, reconstruction strategies, project implementation, and stakeholder management. A conceptual model with the interplay of project success, success traits, as well as their CSFs identified the project management actions that must be monitored during reconstruction. Since the project management approach is widely recognized for PDR projects, these success traits hold huge potential for effective organization and management of housing reconstruction projects. The study also helped to identify project management traits that need improvements for the successful implementation of post-disaster housing projects in Kerala. Thus the research findings can serve as a foundational study for formulating project management strategies appropriate to PDR projects in Kerala.
A Reverse Dynamical Investigation of the Catastrophic WoodSnow Avalanche of 18 January 2017 at Rigopiano, Gran Sasso National Park, Italy
Barbara Frigo, Perry Bartelt, Bernardino Chiaia, Igor Chiambretti, Margherita Maggioni
2021, 12(1): 40-55. doi: 10.1007/s13753-020-00306-6
On 18 January 2017 a catastrophic avalanche destroyed the Rigopiano Gran Sasso Resort & Wellness (Rigopiano Hotel) in the Gran Sasso National Park in Italy, with 40 people trapped and a death toll of 29. This article describes the location of the disaster and the general meteorological scenario, with field investigations to provide insight on the avalanche dynamics and its interaction with the hotel buildings. The data gathered in situ suggest that the avalanche was a fluidized dry snow avalanche, which entrained a sligthtly warmer snow cover along the path and reached extremely long runout distances with braking effect from mountain forests. The avalanche that reached the Rigopiano area was a “wood-snow” avalanche—a mixture of snow and uprooted and crushed trees, rocks, and other debris. There were no direct eyewitnesses at the event, and a quick post-event survey used a numerical model to analyze the dynamics of the event to estimate the pressure, velocity, and direction of the natural flow and the causes for the destruction of the hotel. Considering the magnitude and the damage caused by the event, the avalanche was at a high to very high intensity scale.
Participatory Mapping and Visualization of Local Knowledge: An Example from Eberbach, Germany
Carolin Klonner, Tomás J. Usón, Nicole Aeschbach, Bernhard Höfle
2021, 12(1): 56-71. doi: 10.1007/s13753-020-00312-8
A rise in the number of flood-affected people and areas has increased the interest in new methods and concepts that account for this change. Citizens are integrated into disaster risk reduction processes through participatory approaches and can provide valuable up-to-date local knowledge. During a field study in Eberbach (Baden– Wuerttemberg, Germany) sketch maps and questionnaires were used to capture local knowledge about flooding. Based on a previous study on urban flooding in Santiago de Chile, the tools were adapted and applied to river flooding in the city of Eberbach, which is regularly flooded by the Neckar River, a major river in southwest Germany. The empirical database of the study comprises 40 participants in the study area and 40 in a control area. Half of the participants in each group are residents and half are pedestrians. Purposive sampling was used, and the questionnaires aimed to gather demographic information and explore what factors, such as property, influence the risk perception of the study participants. The results show that residents identify a larger spatial area as at risk than pedestrians, and owning property leads to higher risk awareness. The flood type influenced the choice of the base maps for the sketch maps. For river flooding, one map with an overview of the area was sufficient, while for urban flooding a second map with more details of the area also enables the marking of small streets. The information gathered can complement authoritative data such as from flood models. This participatory approach also increases the communication and trust between local governments, researchers, and citizens.
Resilience-Driven Road Network Retrofit Optimization Subject to Tropical Cyclones Induced Roadside Tree Blowdown
Fuyu Hu, Saini Yang, Russell G. Thompson
2021, 12(1): 72-89. doi: 10.1007/s13753-020-00301-x
This article focuses on decision making for retrofit investment of road networks in order to alleviate severe consequences of roadside tree blowdown during tropical cyclones. The consequences include both the physical damage associated with roadside trees and the functional degradation associated with road networks. A trilevel, two-stage, and multiobjective stochastic mathematical model was developed to dispatch limited resources to retrofit the roadside trees of a road network. In the model, a new metric was designed to evaluate the performance of a road network; resilience was considered from robustness and recovery efficiency of a road network. The proposed model is at least a nondeterministic polynomialtime hardness (NP-hard) problem, which is unlikely to be solved by a polynomial time algorithm. Pareto-optimal solutions for this problem can be obtained by a proposed trilevel algorithm. The random forest method was employed to transform the trilevel algorithm into a singlelevel algorithm in order to decrease the computation burden. Roadside tree retrofit of a provincial highway network on Hainan Island, China was selected as a case area because it suffers severely from tropical cyclones every year, where there is an urgency to upgrade roadside trees against tropical cyclones. We found that roadside tree retrofit investment significantly alleviates the expected economic losses of roadside tree blowdown, at the same time that it promotes robustness and expected recovery efficiency of the road network.
Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI)
Md. Shahinoor Rahman, Liping Di, Eugene Yu, Li Lin, Zhiqi Yu
2021, 12(1): 90-110. doi: 10.1007/s13753-020-00305-7
Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing, food policy, and agricultural trade. The main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed Disaster Vegetation Damage Index (DVDI). By incorporating the DVDI along with information on crop types and flood inundation extents, this research assessed crop damage for three case-study events: Iowa Severe Storms and Flooding (DR 4386), Nebraska Severe Storms and Flooding (DR 4387), and Texas Severe Storms and Flooding (DR 4272). Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa, Nebraska, and Texas. More than half of flooded corn has experienced no damage, whereas 60% of affected soybean has a higher degree of loss in most of the selected counties in Iowa. Similarly, a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming, which is the most affected county in Nebraska. A total of 454 ha of corn are severely damaged in Anderson County, Texas. More than 200 ha of alfalfa have moderate to severe damage in Navarro County, Texas. The results of damage assessment are validated through the NDVI profile and yield loss in percentage. A linear relation is found between DVDI values and crop yield loss. An R2 value of 0.54 indicates the potentiality of DVDI for rapid crop damage estimation. The results also indicate the association between DVDI class and crop yield loss.
Effects of Rainfall and Underlying Surface on Flood Recession—The Upper Huaihe River Basin Case
Yashan Cheng, Yanfang Sang, Zhonggen Wang, Yuhan Guo, Yin Tang
2021, 12(1): 111-120. doi: 10.1007/s13753-020-00310-w
The effects of rainfall and underlying surface conditions on flood recession processes are a critical issue for flood risk reduction and water use in a region. In this article, we examined and clarified the issue in the upper Huaihe River Basin where flood disasters frequently occur. Data on 58 rainstorms and flooding events at eight watersheds during 2006–2015 were collected. An exponential equation (with a key flood recession coefficient) was used to fit the flood recession processes, and their correlations with six potential causal factors—decrease rate of rainfall intensity, distance from the storm center to the outlet of the basin, basin area, basin shape coefficient, basin average slope, and basin relief amplitude—were analyzed by the Spearman correlation test and the Kendall tau test. Our results show that 95% of the total flood recession events could be well fitted with the coefficient of determination (R2) values higher than 0.75. When the decrease rate of rainfall intensity (Vi) is smaller than 0.2 mm/h2, rainfall conditions more significantly control the flood recession process; when Vi is greater than 0.2 mm/h2, underlying surface conditions dominate. The result of backward elimination shows that when Vi takes the values of 0.2–0.5 mm/h2 and is greater than 0.5 mm/h2, the flood recession process is primarily influenced by the basin’s average slope and basin area, respectively. The other three factors, however, indicate weak effects in the study area.
Natural and Socioeconomic Factors and Their Interactive Effects on House Collapse Caused by Typhoon Mangkhut
Xiangxue Zhang, Juan Nie, Changxiu Cheng, Chengdong Xu, Ling Zhou, Shi Shen, Yuan Pei
2021, 12(1): 121-130. doi: 10.1007/s13753-020-00322-6
Typhoons are an environmental threat that mainly affects coastal regions worldwide. The interactive effects of natural and socioeconomic factors on the losses caused by typhoon disasters need further examination. In this study, GeoDetector was used to quantify the determinant powers of natural and socioeconomic factors and their interactive effects on the rate of house collapse in Guangdong and Guangxi Provinces of southeast China caused by Typhoon Mangkhut in 2018. We further identify the dominant factors that influenced the disaster losses. The local indicators of spatial association method was then introduced to explain the spatial heterogeneity of the disaster losses under the influence of the dominant factor. The results indicate that both natural and socioeconomic factors significantly affected the house collapse rate. The maximum precipitation was the dominant factor, with a q value of 0.21, followed by slope and elevation, with q values of 0.17 and 0.13, respectively. Population density and per capita gross domestic product had q values of 0.15 and 0.13, respectively. Among all of the interactive effects of the influencing factors, the interactive effect of elevation and the ratio of brick-wood houses had the greatest influence (q = 0.63) on the house collapse rate. These results can contribute to the formulation of more specific safety and property protection policies.
Spatiotemporal Trend Analysis of Precipitation Extremes in Ho Chi Minh City, Vietnam During 1980–2017
Nguyen Trong Quan, Dao Nguyen Khoi, Nguyen Xuan Hoan, Nguyen Ky Phung, Thanh Duc Dang
2021, 12(1): 131-146. doi: 10.1007/s13753-020-00311-9
In this study, the spatiotemporal variability of trends in extreme precipitation events in Ho Chi Minh City during the period 1980–2017 was analyzed based on several core extreme precipitation indices (Rx1day, Rx5day, CDD, CWD, R20mm, R25mm, R95p, and SDII). The nonparametric Mann–Kendall and Sen’s slope methods were used to compute the statistical strength, stability, and magnitude of trends in annual rainfall, as well as the extreme precipitation indices. We found that 64% of the stations had statistically significant upward trends in yearly rainfall, with high magnitudes frequently observed in the northern and southern regions of the city. For the extreme precipitation indices, only SDII and R25mm showed dominantly significant trends. Additionally, there were increasing trends in the frequency and duration at the southern and central regions of the city during the study period. Furthermore, El Niño-Southern Oscillation and Pacific Decadal Oscillation positively correlated with the duration and negatively correlated with the intensity and frequency of extreme precipitation. Thus, water management plans should be adjusted appropriately to reduce the severe impacts of precipitation extremes on communities and ecosystems.
COVID-19 and Ecosyndemic Vulnerability: Implications for El Niño-Sensitive Countries in Latin America
Ivan J. Ramírez, Jieun Lee
2021, 12(1): 147-156. doi: 10.1007/s13753-020-00318-2
Latin America has emerged as an epicenter of the COVID-19 pandemic. Brazil, Peru, and Ecuador report some of the highest COVID-19 rates of incidence and deaths in the region. These countries also face synergistic threats from multiple infectious diseases (that is, ecosyndemic) and quasi-periodic El Niño-related hazards every few years. For example, Peru, which is highly sensitive to El Niño, already copes with an ecosyndemic health burden that heightens during and following weather and climate extreme events. Using an ecosyndemic lens, which draws on a multi-disease hazard context of place, this commentary highlights the importance of El Niño as a major factor that not only may aggravate COVID-19 incidence in the future, but also the broader health problem of ecosyndemic vulnerability in Latin America.