2014 Vol. 5, No. 4

Display Method:
Capacities in Facing Natural Hazards: A Small Island Perspective
Mercy M. F. Rampengan, Agni Klintuni Boedhihartono, Lisa Law, J. C. Gaillard, Jeffrey Sayer
2014, 5(4): 247-264. doi: 10.1007/s13753-014-0031-4
Isolated communities on small islands are often characterized as vulnerable and marginalized. We studied the recent history of Laingpatehi, a village on Ruang Island off the north coast of Sulawesi, Indonesia to show that the marginalization-vulnerability nexus can be offset by capacity and social cohesion to enable sustainable livelihoods. The island has been impacted by volcanic eruptions, earthquakes, and competition for marine resources from mainland-based fishermen. The community has shown a remarkable ability to cope and prosper in the face of a series of external hazards. We used a sustainable livelihoods approach to identify the assets that enabled the villagers to cope. Strong social cohesion was central to the ability to organize the community and confront hazards. A diversified livelihood strategy drawing on the small island environment and its coastal and marine resources, income generating activities in a distant satellite village, and significant remittances from employment in other parts of Indonesia underpinned people's capacities to face hazards. Government assistance played a supporting role. The case of Laingpatehi demonstrates how remoteness, rather than being a source of vulnerability, can provide access to existing resources and facilitate innovation. Disaster risk reduction strategies should focus more on reinforcing these existing capacities to deal with hazards and less on physical protection and postdisaster responses.
Modeling Impact of Hurricane Damages on Income Distribution in the Coastal U.S.
Tatjana Miljkovic, Dragan Miljkovic
2014, 5(4): 265-273. doi: 10.1007/s13753-014-0030-5
This article examines the impact of catastrophic hurricane events on income distribution in hurricane states in the United States. Media claims have been made and the perception created that the most damaging impact of hurricanes is on the lowest income population in the affected states. If these claims are true, they may have serious implications for the insurance industry and government policy makers. We develop a panel data, fixed effects econometric model that includes hurricane-impacted states as cross-sections using annual data for a period of almost 100 years. The Gini coefficient is used as a measure of income inequality, and is a function of normalized hurricane economic damages, gross domestic product (GDP), a set of socioeconomic variables that serves as a control, time trend, and cross-sectional dummy variables. Findings indicate that for every 100 billion US dollars in hurricane economic damages there is an increase in income inequality by 5.4 % as measured by Gini coefficient. Political, sociodemographic, and economic variables are also significant. These include such variables as the political party controlling the U.S. Senate, the proportion of nonwhite population by state, and GDP. Time trend is a positive and significant variable, suggesting an increase in income inequality over time. There are significant differences among the states included in the study. Our results demonstrate that different segments of the population are differently impacted by hurricanes and suggest how that differential impact could be considered in future government policies and business decisions, particularly those made by the insurance industry.
Convection–Diffusion Model for the Prediction of Anthropogenically-Initiated Wildfire Ignition
Ravi Sadasivuni, Shanti Bhushan, William H. Cooke
2014, 5(4): 274-295. doi: 10.1007/s13753-014-0034-1
A spatial interaction model to predict anthropogenically-initiated accidental and incendiary wildfire ignition probability is developed using fluid flow analogies for human movement patterns. Urban areas with large populations are identified as the sites of global influencing factors, and are modeled as the gravity term. The transportation corridors are identified as local influencing factors, and are modeled using fluid flow analogy as diffusion and convection terms. The model is implemented in ArcGIS, and applied for the prediction of wildfire hazard distribution in southeastern Mississippi. The model shows 87 % correlation with historic data in the winter season, whereas the previously developed gravity model shows only 75 % correlation. The normalized error for convection–diffusion model predictions is about 5 % in the winter season, whereas the gravity model shows an error of 7 %. The proposed model is robust as it couples a multi-criteria behavioral pattern within a single dynamic equation to enhance predictive capability. At the same time, the proposed model is more costly than the gravity model as it requires evaluation of distance from intermodal transportation corridors, transportation corridor density, and traffic volume maps. Nonetheless, the model is developed in a modular fashion, such that either global or local terms can be neglected if required.
Impact of Temporal Population Distribution on Earthquake Loss Estimation: A Case Study on Sylhet, Bangladesh
Sharmin Ara
2014, 5(4): 296-312. doi: 10.1007/s13753-014-0033-2
To estimate human loss in an earthquake-prone area, it is necessary to analyze the role played by the spatiotemporal distribution of the area's resident population. In order to evaluate earthquake impact, this article focuses on the spatiotemporal distribution of population and five scenario earthquakes that form the basis for loss estimation in the city of Sylhet, Bangladesh. Four temporal contexts (weekday, weekly holiday, the 30 days of Ramadan, and strike days) expand the more typical daytime and nighttime settings in which to examine hazard risk. The population distribution for every 2 hour interval in a day is developed for each type of day. A relationship between the occupancy classes and average space (persons per 100 m2) is used to distribute people in each building regardless of building locations. A total daytime and nighttime population is obtained for each building and the estimated nighttime population is used to model the population for four temporal scenarios in a year based on different factors and weights. The resulting data are employed to estimate population loss for each of the temporal and earthquake scenarios. This study used building-specific human vulnerability curves developed by the Central American Probabilistic Risk Assessment (CAPRA) to obtain possible loss of life estimates. The results reveal that there is a high positive correlation between the spatiotemporal distribution of population and the potential number of casualties.
Built-in Risk: Linking Housing Concerns and Flood Risk in Subsidized Housing Settlements in Cape Town, South Africa
Robyn Pharoah
2014, 5(4): 313-322. doi: 10.1007/s13753-014-0032-3
As in many other settings in developing countries, discussions on urban flooding in South Africa tend to focus on informal settlements. There is less attention to poor but formal housing areas, based on the largely untested assumption that the formalization of housing addresses risk. This is at odds with an extensive literature from the housing and developmental sectors that highlights weaknesses in the location and construction of low-income housing, particularly state-subsidized housing. Drawing on research in 10 poor, flood-prone settlements in Cape Town, South Africa, this article explores whether providing housing addresses risk. The results show that flooding remains a challenge in subsidized housing areas and that risk is linked strongly to the buildings themselves. Poorly designed and constructed dwellings perpetuate risk in lowincome areas. While divorced conceptually and practically, disaster risk and housing issues are critically linked, and housing concerns must be factored into discussions on flooding in Cape Town and comparable settings elsewhere.
Assessment and Mapping of Potential Storm Surge Impacts on Global Population and Economy
Jiayi Fang, Shao Sun, Peijun Shi, Jing'ai Wang
2014, 5(4): 323-331. doi: 10.1007/s13753-014-0035-0
With global climate change, population growth, and economic development in the twenty-first century, large cyclonic storm surges may result in devastating effects in some coastal areas of the world. However, due to the deficiency of global data and large-scale modeling efforts, the assessment and mapping of potential storm surge impacts at the global level are limited. In this article, the potential inundated area of global coastal zones is projected using information diffusion theory, based on the historical hourly sea-level observation records from the University of Hawaii Sea Level Center (UHSLC), considering variations in coastal morphology and tropical cyclone tracks. Combined with global demographic and GDP data, population and GDP at risk of storm surge impacts are calculated, mapped, and validated through the comparison with historical losses. The resulting potential impact maps provide a preliminary outlook on risks that may help governments of countries to make storm surge disaster prevention and reduction plans.