Multi-hazard Events Research Articles

Building climate resilience of UK’s rail network through a multihazard approach

Climate projections suggest that the UK will be increasingly affected by climate-induced hazards in the future, exacerbating the potential for multihazard events to occur. This paper explores the extent to which climate resilience is being embedded in current and future rail transport infrastructure projects in London through use of a multihazard approach. It concludes that a multihazard approach is a relatively new concept and is therefore not currently being considered widely. However, opportunities for decision makers to explore its value to mitigate climate risks through collaborative working and further research exist – this paper recommends that two main stages are required to achieve this. Route 1 requires climate/hazard experts to identify and characterise the different hazard interactions that could occur, including their impact on the UK/London. Following this, route 2 involves the creation of multidisciplinary workshops to identify any multihazard vulnerabilities associated with different rail assets – potentially highlighting areas where further research is required. The paper concludes that although a London rail context has been adopted in order to understand the relevance of a multihazard approach, there is opportunity for it to be considered across many different sectors in the UK and the wider civil engineering industry.

Nepal, a multi-hazard risk country: Spatio-temporal analysis

Nepal, though covers small area of the earth, exposes complex geology with active tectonic processes, high peaks, sloppy terrain and climatic variation. Combination of such geo-physical and climatic conditions with existing poor socio-economic conditions, unplanned settlements, rapidly increasing population and low level of awareness has put the country in highest risk to multi-hazard events. Fires, floods, landslides and epidemics are the most frequent hazard events, which have cumulatively caused a significant loss of lives and property every year. However, due to diversity in physiographic, climatic and socio-economic conditions within the country, the type, frequency and degree of the impact of such events differs in different places. During the period of 46 years (1971-2016), an average of 2 events have been occurred causing 3 deaths/missing every day. Disaster events occurred most frequently during the months of April, July and August, while relatively lesser number of events have been reported during January, November and December. However, earthquakes have been reported in different months, regardless with the season.
 This paper is an effort to analyse the spatial distribution and temporal variation of disaster events in Nepal. Further it has drawn a trend of disasters occurrence in Nepal, which will help the decision makers and other stakeholders for formulating Disaster Risk Management (DRM) plan and policies on one hand and heighten citizens’ awareness of against disasters on the other.

Maximizing the Resilience of Healthcare Workers in Multi-hazard Events: Lessons from the 2014-2015 Ebola Response in Africa.

There is increasing knowledge that health care workers (HCWs) can experience a variety of emotional impacts when responding to disasters and terrorism events. The Anticipate, Plan and Deter (APD) Responder Risk and Resilience Model was developed to provide a new, evidence-informed method for understanding and managing psychological impacts among HCWs. APD includes pre-deployment development of an individualized resilience plan and an in-theater, real-time self-triage system, which together allow HCWs to assess and manage the full range of psychological risk and resilience for themselves and their families. The inclusion of objective mental health risk factors to prompt activation of a coping plan, in connection with unit leadership real-time situational awareness, enables the first known evidence-driven "targeted action" plan to address responder risk early before Post Traumatic Stress Disorder and impairment become established. This paper describes pilot work using the self-triage system component in Alameda County's Urban Shield and the Philippines' Typhoon Haiyan, and then reports a case example of the full APD model implementation in West Africa's Ebola epidemic.

Combined Wind-Wave-Surge Hurricane-Induced Damage Prediction for Buildings

AbstractCoastal structures are subjected to multihazard events such as hurricanes which consist of hurricane-induced surge and waves as well as winds. Hurricanes are a common natural hazard in the ...

The performance of concrete filled steel tube columns under post-earthquake fires

In this study, a nonlinear three-dimensional finite element (FE) model was developed and validated to investigate the response of concrete filled tube (CFT) columns subjected to post-earthquake fires. Three steps were considered successively in the modelling, namely, cyclic, thermal and structural analyses. Outputs from the cyclic loading including residual deformations were imposed as an initial condition to the thermal-stress model, imitating the seismic response of the column. Subsequently, a nonlinear sequentially thermal-stress analysis was conducted to simulate the fire response of column after the earthquake. The proposed FE model was validated by comparing the simulation results with the observations of full-scale fire and cyclic tests available in the literature. The validated numerical model was then used to study the behavior of CFT columns under the combined action of earthquake and fire as a multi-hazard event. Three probable seismic damage scenarios were considered in the column, namely, middle length, bottom and top end region damages. The level of damage was assumed as a high damage level, presuming that the column reached 50% of its lateral resistance while still maintaining its overall stability after the earthquake. The results showed that the top and bottom end region damages have not significantly influence the fire response of the damaged column. Besides, the column with the middle span damage performed a lesser fire resistance time owing to the coincidence of damage location to that of onset of global buckling.

Data-driven probabilistic post-earthquake fire ignition model for a community

Fire following earthquake (FFE), a cascading multi-hazard event, can cause major social and economical losses in a community. In this paper, two existing post-earthquake fire ignition models that are implemented in Geographic Information System (GIS) based platforms, Hazus and MAEViz/Ergo, are reviewed. The two platforms and their FFE modules have been studied for suitability in community resiliency evaluations. Based on the shortcomings in the existing literature, a new post-earthquake fire ignition model is proposed using historical FFE data and a probabilistic formulation. The procedure to create the database for the model using GIS-based tools is explained. The proposed model provides the probability of ignition at both census tract scale and individual buildings, and can be used to identify areas of a community with high risk of fire ignitions after an earthquake. The model also provides a breakdown of ignitions in different building types. Finally, the model is implemented in MAEViz/Ergo to demonstrate its application in a GIS-based software.

Real-time Application of the Multihazard Hurricane Impact Level Model for the Atlantic Basin

Tropical cyclones are an example of a multi-hazard event with impacts that can highly vary depending on landfall location, wind speed, storm surge, and inland flooding from precipitation. These storms are typically categorized by their wind speed and pressure, while evacuation orders are typically given based on storm surge. The general public relies on these single hazard assessment parameters when attempting to understand the risk of an oncoming event. However, after the fact, these events are ranked by economic damage and death toll. Therefore, it is imperative that when these events are communicated to the public, during the forecast period, the multiple hazards are incorporated in terms the public can easily associate with, such as economic damage. This paper provides an evaluation on the potential for real-time use of artificial neural networks, through the utilization of an already developed Hurricane Impact Level Model, to forecast a range of economic damage from tropical cyclone events, during the 2015 and 2016 United States Hurricane Season. The Hurricane Impact Level Model is built prior to the start of each season and simulated every three hours, in conjunction with National Hurricane Center issued advisories, for oncoming tropical cyclones forecasted to make landfall. Weaker and more common tropical cyclones have a less varied forecast and produce more accurate Impact Level predictions. More complicated and uncertain events, such as 2016 Hurricane Matthew, require the user’s discretion in communicating varying landfall locations for a complex track forecast to the model. As National Hurricane Center (NHC) forecasts change with respect to both track and meteorological hazards affecting land, the estimated Impact Level and the Hurricane Impact Level (HIL) model confidence will also change. In other words, if a track shifts to a more vulnerable location, or to more locations, or the meteorological hazards increase, the Impact Level will subsequently increase. All tropical cyclones from the 2015 and 2016 seasons demonstrate the validity of the Hurricane Impact Level Model with a forecast confidence of at least 60% for up to 30 hours out from an impending landfall as well as reliability for real-time use, if data is available.

Resilience of tall steel moment resisting frame buildings with multi-hazard post-event fire

Infrastructure resilience is the ability of an infrastructure asset to limit the effect and duration of damaging extreme events. The four main components of the concept of resilience are robustness, resourcefulness, recovery and redundancy most of which are very difficult to be quantified. In addition to this difficulty, a careful study of extreme event cases can demonstrate that it is very common for an extreme event scenario to include cascading multi-hazard events such as blast, floods, earthquake, and fire. This paper studies the resilience of a multi-story steel frame with multi-hazard considerations which include a post-event fire scenario. The initiating extreme event is simulated through the threat-independent alternate load path method of analysis and a post-event fire is considered following the extreme event. The work in the paper combines previous work by the authors on stability-induced collapse of damaged steel structures and closed-form solutions for temperature predictions of wide-flange components during a fire scenario. For the purposes of the fire scenarios, new fire time-temperature curves are developed based on experimental data from the well-known Cardington fire tests. The results show that even when a structure can withstand an extreme event scenario, a post-event fire consideration is highly critical to evaluate the remaining time of survival of the structure before collapse. It is shown that the sequential method of multi-hazard analysis can lead to very short available time periods before the post-event fire leads to the complete collapse.

Stochastic Modeling of Road System Performance during Multihazard Events: Flash Floods and Earthquakes

AbstractTransport resilience is an important area of research in the global effort to adapt to climate change. This paper introduces and applies a stochastic modeling methodology to assess the impa...

Multihazard Interaction Effects on the Performance of Low-Rise Wood-Frame Housing in Hurricane-Prone Regions

AbstractHurricanes represent multihazard events that include wind, windborne debris, storm surge, and rainfall hazards. Conventional risk analysis does not consider the interaction between these mu...

Reconstruction of a flash flood event through a multi-hazard approach: focus on the Rwenzori Mountains, Uganda

The increased use of complex and holistic modelling for multi-hazard analysis is in sharp contrast with a lacuna in hazard analysis in equatorial Africa. This study aims to increase understanding of multi-hazard events in poorly documented regions with low accessibility. We focus on the Nyamwamba catchment (107 km2) located in the Rwenzori Mountains (Uganda) where on May 1, 2013, a severe flash flood occurred. In this region, wildfires, earthquakes and landslides occur as well. Here we reconstruct the circumstances under which this flash flood event was triggered, characterize the different processes acting upon the catchment dynamics and estimate the damaging effects of the flash flood within the catchment. The combined occurrence of intense rainfall, a forest fire having burned 18 % of the catchment area and the occurrence of 29 landslides providing debris to the river system, induced a debris-rich and very destructive flash flood which caused several fatalities, the destruction of 70 buildings, several bridges, a hospital, a school, a tarmac road and several lifelines. Although the methodologies applied to estimate peak discharge, detect landslides and delineate wildfires are well established in their disciplines and sometimes limited in their precision, their combination is required to demonstrate the importance of the wildfire and landslides for the magnitude of this flood, unprecedented in decades but characterized by a low return period of the triggering rainfall event. This indicates that flash floods should not be considered as self-determined phenomena but as a result of several cascading and interacting hazard processes.

Using artificial neural networks to forecast economic impact of multi-hazard hurricane-based events

In multi-hazard events, it remains difficult to communicate the collective effect these hazards have on the envisioned outcome or impact to the public. Currently, there are multiple models in use by emergency management and other government personnel to predict effects of hazards that put emphasis on wind damage (just as the Saffir–Simpson scale does), which tend to leave out the non-wind driven precipitation hazard. Experts who work with hazard events consistently build a knowledge base over time from experience that accounts for the collective effects of these multiple hazards in relation to locational vulnerabilities. In this study, an original artificial neural network is developed and used in an effort to mimic the previously mentioned learned and experienced-based knowledge. The output from the neural network model is an Impact Level Ranking System that ranks hurricanes based on total economic damage. The use of population affected, landfall location(s), wind speed, pressure, storm surge, and precipitation for inputs with a final Bayesian Regulation training approach allows for an ability to forecast multi-hazard hurricane events in terms the public could comprehend while remaining thorough in all hazards.

Impacts of cognitive and behavioural biases on decision-makers prior to the 1995 Kobe, the 2004 Sumatra-Andaman and the 2011 Tohoku earthquakes

Current multi-risk disasters, such as the 2011 Tohoku earthquake, tsunami and nuclear disaster and the 2004 Sumatra-Andaman earthquake and tsunami, showed the need of a multi-risk approach in hazards mitigation and management, which makes the decision-making process on assessment of probabilities and making choices for risks mitigation even more complex. This motivated us to focus on two research questions. The first one is if stakeholders treat such hazards separately from each other, neglecting interdependencies and frequent causal, spatial and temporal relationships between different kinds of risk. The second one is how their behavior is influenced by different kinds of behavioral biases, leading to false estimations of probabilities of multi-hazard events and further choices in the decision-making process in regards to the multi-risk mitigation and management. Our methodology to approach these questions included case study method to identify cognitive and behavioral biases in frames of historical multi-risk disasters and conceptual content analysis to understand, which from the identified biases were the most influential in the decision-making process. We first conducted stakeholder interview to identify cases of multi-risk decision-making, then we conducted content analysis and case studies to understand how various cognitive and behavioral biases hampered risk governance and management in the identified cases. Our results showed that availability heuristics, loss aversion and limited worry were three most common biases, at the same time as experimental versus statistical evidence and bounded rationality were playing the least significant role in the decision-making process.

Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectral–textural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes.

Determination of Coastal Belt in the Disaster Prone Area: A case study in the Coastal area of Bantul Regency, Yogyakarta, Indonesia

Mitigation strategy development is needed for protecting coastal communities and preserving coastal resourc-es from the impact of coastal hazards. It is believed that coastal land use planning can be proposed to reduce impact of coastal hazards. ἀe coastal belt boundary development is one of the mitigation approach which is common use in many countries as an inexpensive solution compared to hard structure development. Our study was located in the coastal area of Bantul regency, a low lying coastal area and densely populated region, where some clustered people lives proximity to the sea. In this study, a new method for determining coastal belt boundary was developed in the study area. ἀe Spatial Multi Criteria Evaluation (SMCE) tool of Ilwis plays as powerful tool for assessing coastal vulnerability in term of multi-hazard events. To calculate the width of the coastal belt boundary, the Ilwis-SMCE outcomes were employed. By apply-ing this method, there were three scenarios of the setbacks wide for both direct and pairwise method to be proposed. In general, the coastal area of Bantul Regency is dominated by high vulnerable area respectively impacted by multi-hazard events (tsunami, high wave and erosion-accretion), by incorporating some coastal features, such as topography, bio-physics, economic, and social-culture. ἀe results of the analysis are consistent with what was observed in the actual areas. Taken into account for hazard and vulnerability indices, the coastal belt boundary established in this area should wider than those already determined by existing regulations.