Showing papers in "Natural Hazards in 2018"

What determines flood risk perception? A review of factors of flood risk perception and relations between its basic elements

Abstract: While the methods of risk analysis are generally based on objective measurements, the subjective assessment of risk, such as risk perception, is currently considered a crucial aspect in the context of flood risk management. Risk perception is regarded as an assessment of the perceived probability of hazard and the perceived probability of the results (most often—negative consequences). The work attempts to answer the question: What determines flood risk perception? The knowledge of the factors influencing flood risk perception can solve the issue of the society’s underestimation of flood risk. This issue was considered both in terms of the impact of particular factors on flood risk perception and the interrelationship between three characteristics of flood risk perception: preparedness, worry and awareness. The results were developed based on critical analysis of the empirical research. The review shows that the way particular characteristics determine flood risk perception is not clear and many authors show the diverse conclusions from the similar research. Taking into account various research results, the following factors were distinguished: primary (which clearly influence risk perception), secondary (which influence it unclearly and require further research) and intervening (often describing the context). The organization of the results of the research on the flood risk assessment conducted herein aims to improve the understanding of the human perception of flood risk and, as a result, will lead to the decrease in flood risk by improving the communication of the issue and motivating the residents of the endangered areas to take actions that reduce the negative effects of floods.

Energy production, economic growth and CO 2 emission: evidence from Pakistan

Abstract: An extensive body of knowledge is available on the relationship between energy consumption and CO2 emission incorporated by different variables. However, the role of energy production in the pollution equation is largely unknown. The present work quantifies the relationship between energy production, economic growth and CO2 emission. A family of econometric tools is used to achieve the objective of the study. Due to the sensitivity of objective of the present work, we use structural break unit root test to measure the stability of parameters within the time span of 1970–2011. Johansen cointegration test confirms the existence of cointegration among variables. Autoregressive distributive lag model reveals that energy production from the fossil fuel is the main culprit behind growing CO2 emission. Additionally, the finding of the study claims the existence of environmental Kuznets curve hypothesis in the significance of energy production in Pakistan. Moreover, bidirectional causality is detected between energy production and carbon dioxide emission in the long-run path. It is suggested that pollution can be condensed by producing energy from the renewable source (hydropower, solar power, geothermal and wind energy) and add more renewable energy to the energy mix.

The role of social capital, personal networks, and emergency responders in post-disaster recovery and resilience: a study of rural communities in Indiana

Abstract: The factors that explain the speed of recovery after disaster remain contested. While many have argued that physical infrastructure, social capital, and disaster damage influence the arc of recovery, empirical studies that test these various factors within a unified modeling framework are few. We conducted a mail survey to collect data on household recovery in four small towns in southern Indiana that were hit by deadly tornadoes in March 2012. The recovery effort is ongoing; while many of the homes, businesses, and community facilities were rebuilt in 2013, some are still under construction. We investigate how households in these communities are recovering from damage that they experienced and the role of social capital, personal networks, and assistance from emergency responders on the overall recovery experience. We used an ordered probit modeling framework to test the combined as well as relative effects of (a) damage to physical infrastructures (houses, vehicles, etc.); (b) recovery assistance from emergency responders (FEMA) as well as friends and neighbors; (c) personal network characteristics (size, network density, proximity, length of relationship); (d) social capital (civic engagement, contact with neighbors, trust); and (e) household characteristics. Results show that while households with higher levels of damage experienced slower recovery, those with recovery assistance from neighbors, stronger personal networks, and higher levels of social capital experienced faster recovery. The insights gained in this study will enable emergency managers and disaster response personnel to implement targeted strategies in facilitating post-disaster recovery and community resilience.

Flood hazards: household vulnerability and resilience in disaster-prone districts of Khyber Pakhtunkhwa province, Pakistan

Abstract: Pakistan is alarmingly exposed and vulnerable to flood disasters as a result of rapid urbanization that has not taken into account the threats posed by climate change. The devastating impacts of floods and other natural disasters put extra pressure on the country’s budget and has driven the country’s leadership to adopt a proactive approach instead of traditional, aid-based, approach, one that encourages the inclusion of disaster risk reduction measures within local disaster management policies. This research elaborates household vulnerability and resilience to flood disaster within two districts within Pakistan. It uses a dataset of 600 households collected through face-to-face interviews from two districts within the Khyber Pakhtunkhwa province that were severely affected by the 2010 flood and data from the Directorate of Khyber Pakhtunkhwa Provincial Disaster Management Authority. In a second step, we assigned weights to the selected variables for vulnerability (exposure, susceptibility and adaptive capacity) and resilience (with social, physical, economic, and institutional components) and used a subjective method (based on expert judgment) to weight these. The survey findings revealed that both study areas were highly vulnerable and had low resilience to flood disasters. The study findings indicated that community households in the flood-prone areas of Nowshera district were more vulnerable and less resilient than those in Charsadda, with a higher composite vulnerability index scoring and a lower composite resilience index score. This study shows that provincial and local disaster management authorities can play a vital role in reducing vulnerability and that more efforts are required to strengthen social, physical, economic, and institutional resilience through capacity-building training, preparedness, and awareness building about preventing and mitigating flood damage.

Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India

Abstract: Flood, a perennial phenomenon mainly in low lying deltaic areas and flood plain regions, can be viewed as beneficial for enhancing soil fertility and agricultural production, but also as one of the most destructive natural hazard endangering human life, property, economy and environment. Floods in lower Gangatic flood plain are annual event, especially Malda district of West Bengal has been severely affected by flood over the years by the mighty Ganga and its left bank tributaries during high stage of flow. Assessing risk from flood using composite hazard and vulnerability index has been a widely recognized tool which acts as an important element for formulation of policies aiming at flood risk reduction. The present investigation is an endeavor to assess risk due to flooding using analytical hierarchical processes incorporating flood hazard elements and vulnerability indicators in geographical information system environment. Flood hazard map has been prepared using selected morphological and hydrometerological elements whereas the vulnerability map has been produced using demographic, socio-economic and infrastructural elements. Finally, risk map of flood has been developed compiling both the above-mentioned aspects. The analysis concluded that the northern and western parts of the district are most risk prone from flood hazard than the eastern part.

Analysis of flash flood disaster characteristics in China from 2011 to 2015

Abstract: Flash floods are one of the most disastrous natural hazards and cause serious loss of life and economic damage every year. Flooding frequently affects many regions in China, including periodically catastrophic events. An extensive compilation of the available data has been conducted across various hydroclimatological regions to analyze the spatiotemporal characteristics of flash floods in China. This inventory includes over 782 documented events and is the first step toward establishing an atlas of extreme flash flood occurrences in China. This paper first presents the data compilation strategy, details of the database contents, and the typical examples of first-hand analysis results. The subsequent analysis indicates that the most extreme flash floods originate mainly from small catchments over complex terrains and results in dominantly small- and medium-sized flooding events in terms of scales; however, these events, abrupt and seasonally recurrent in nature, account for a large proportion of the overall flooding-related disasters, especially disproportionately affecting elderly and youth populations. Finally, this study also recommends several immediate measures could be implemented to mitigate high impacts of deadly flash floods, although it still requires long-term significant efforts to protect human life and property in a country like China.

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

Abstract: In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while onethird of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.

Rumor response, debunking response, and decision makings of misinformed Twitter users during disasters

Abstract: The rapid spread of rumors occurring on social media is a critical problem that poses a great risk to emergency situation navigation, especially during disasters. Many research questions, such as how misinformed users judge potential rumors or how they respond to them, are crucial issues for crisis communication, but have not been extensively studied. This paper fills this gap by originally documenting and studying Twitter users’ rumor and debunking response behaviors during disasters, such as Hurricane Sandy in 2012 and the Boston Marathon bombings in 2013. To this end, two rumors from each disaster and their related tweets are documented for analysis. Users who were misinformed and involved in the rumor topic by posting tweet(s), could respond to a rumor by: (1) spreading (85.86–91.40%), (2) confirmation-seeking (5.39–9.37%), or (3) doubting (0.71–8.75%). However, if the rumor-spreading users were debunked, they would respond by: (1) deleting rumor tweet(s) (2.94–10.00%), (2) clarifying rumor information with a new tweet (0–19.75%), or (3) neither deleting nor clarifying (78.13–97.06%). We conclude that Twitter users perform poorly in rumor detection and rush to spread rumors. The majority of users who spread rumors do not take further action on their Twitter accounts to fix their rumor-spreading behaviors.

Response characteristics and preventions for seismic subsidence of loess in Northwest China

Abstract: Seismic subsidence of loess had been verified by microstructure characteristic, dynamic triaxial test and in situ simulation test using blasting vibration. It has gradually become a significant subject in the field of geotechnical earthquake engineering. Loess is widely distributed in China, which typically has a loose honeycomb-type meta-stable structure that is susceptible to a large reduction in total volume or subsidence upon ground motion. Seismic subsidence contributes to various problems to infrastructures that are constructed on loess. This paper provides a review of state-of-the-art work on mechanism, microstructure characteristic and physical mechanics mechanism of the seismic subsidence. Furthermore, the comprehensive explanation, basics and earlier research performed on subsidence amount estimation, seismic subsidence assessment and corresponding preventions of disasters have been presented briefly. The literature review shows that some significant problems, for example, appropriate theoretical basis, multi-variable coupling in assessment, physical processes, mechanical mechanism in estimation, and so on require constant research and development work to overcome the aforementioned factors. Specifically, research on quantitative relation between macro-mechanics and microstructure cannot proceed only from experimental parameters but should establish theoretical connection between them. Further study on seismic subsidence must be developed under the theory of unsaturated soil mechanics. In addition, research on chronological and spatial development law of large-scale seismic subsidence, prediction of subsidence value and anti-seismic analysis of underground structures can be conducted in future.

Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models

Abstract: The main purpose of this study is to compare the performance of two statistical analysis models like weight of evidence and logistic regression (LR) with a soft computing model like artificial neural networks for landslide susceptibility assessment. These models were applied for the Selinous River drainage basin (northern Peloponnese, Greece) in order to map landslide susceptibility and rate the importance of landslide causal factors. A landslide inventory was prepared using satellite imagery interpretation and field surveys. Eight causal factors including altitude, slope angle, slope aspect, distance to road network, distance to drainage network, distance to tectonic elements, land cover, and lithology were considered. Model performance was tested with receiver operator characteristic analysis. The validation findings revealed that the three models show promising results since they give good accuracy values. However, the LR model proved to be relatively superior in estimating landslide susceptibility throughout the study area.

Statistical analysis of large accidents in China’s coal mines in 2016

Abstract: Mining is a high-risk industry, and mine accidents occur frequently. To better understand the characteristics and trends of current coal mine accidents, 29 cases of significant accidents occurred in China in 2016 are introduced first in this manuscript; then, the accident types, occurrence time, occurrence locations, and direct causes were analyzed for these accidents. Finally, according to the analysis results, coal mine accident prevention and control suggestions are presented. This data analysis not only plays a positive role in the prevention and control of mine accidents in China but also has reference significance for the safe production of coal mines in other countries of the world.

A resilience optimization model for transportation networks under disasters

Abstract: Natural and/or man-made disasters have caused serious problems in transportation systems due to their unpredictable and destructive characteristics. Under disasters, transportation infrastructure plays an important role in emergency management; however, this infrastructure is also vulnerable because of disasters. One way to describe the vulnerable is through resilience. Resilience refers to the ability to recover from a disruption under unexpected conditions, such as natural and/or man-made disasters. How to enhance resilience of transportation infrastructure under disasters is an important issue when facing natural or man-made disasters. This study aims to measure and optimize transportation resilience under disasters. An optimization model for resilience under the constraints of budget and traversal time is proposed. One special feature is that preparedness and recovery activities are implicitly considered and incorporated within the optimization model. The mathematical model provides a good connection between preparedness/recovery activities and network-level resilience. In order to illustrate the proposed model, a real city network and assumptions on activities of emergency management are used in a series of numerical experiments. Traffic conditions before and after disasters are evaluated by the simulation-assignment model, DynaTAIWAN. Experiments and results illustrate advantages for network-level transportation resilience assessment and also prioritize preparedness and recovery activities under budget constraints.

A conceptual model of avalanche hazard

Abstract: This conceptual model of avalanche hazard identifies the key components of avalanche hazard and structures them into a systematic, consistent workflow for hazard and risk assessments. The method is applicable to all types of avalanche forecasting operations, and the underlying principles can be applied at any scale in space or time. The concept of an avalanche problem is introduced, describing how different types of avalanche problems directly influence the assessment and management of the risk. Four sequential questions are shown to structure the assessment of avalanche hazard, namely: (1) What type of avalanche problem(s) exists? (2) Where are these problems located in the terrain? (3) How likely is it that an avalanche will occur? and (4) How big will the avalanche be? Our objective was to develop an underpinning for qualitative hazard and risk assessments and address this knowledge gap in the avalanche forecasting literature. We used judgmental decomposition to elicit the avalanche forecasting process from forecasters and then described it within a risk-based framework that is consistent with other natural hazards disciplines.

GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping

Abstract: Rampant pasture burning has lead to various forest fires taking their toll over the health of many forests. Nanda Devi Biosphere Reserve, located in the northern part of India, witnessed a majority of these incidents in the recent past, though, it remains comprehensively untouched from research studies. The scale of these wildfires has led to an immense requirement of preventive measures to be taken for recuperating from such events. This requires for an in-depth analysis of the study area, its history of wildfires and their causes. These efforts would assist in laying a blueprint for a contingency plan in the event of a wildfire. This work proposes an evolutionary optimized gradient boosted decision trees for preparing wildfire susceptibility maps for the study area that would aid in the government’s forest preservation and disaster management activities. The study took 18 ignition factors of elevation, slope, aspect, plan curvature, topographic position index, topographic water index, normalized difference vegetation index, soil texture, temperature, rainfall, aridity index, potential evapotranspiration, relative humidity, wind speed, land cover and distance from roads, rivers and habitations into consideration. The study revealed that approximately 1432.025 km2 of area was very highly susceptible to forest fires while 1202.356 km2 was highly susceptible to forest fires. The proposed model was compared against various machine learning models such as random forest, neural networks and support vector machines, and it outperformed them by achieving an overall accuracy of 95.5%. The proposed model demonstrated good prospects for application in the field of hazard susceptibility mappings.

Coupling sentiment and human mobility in natural disasters: a Twitter-based study of the 2014 South Napa Earthquake

Abstract: Understanding population dynamics during natural disasters is important to build urban resilience in preparation for extreme events. Social media has emerged as an important source for disaster managers to identify dynamic polarity of sentiments over the course of disasters, to understand human mobility patterns, and to enhance decision making and disaster recovery efforts. Although there is a growing body of literature on sentiment and human mobility in disaster contexts, the spatiotemporal characteristics of sentiment and the relationship between sentiment and mobility over time have not been investigated in detail. This study therefore addresses this research gap and proposes a new lens to evaluate population dynamics during disasters by coupling sentiment and mobility. We collected 3.74 million geotagged tweets over 8 weeks to examine individuals’ sentiment and mobility before, during and after the M6.0 South Napa, California Earthquake in 2014. Our research results reveal that the average sentiment level decreases with the increasing intensity of the earthquake. We found that similar levels of sentiment tended to cluster in geographical space, and this spatial autocorrelation was significant over areas of different earthquake intensities. Moreover, we investigated the relationship between temporal dynamics of sentiment and mobility. We examined the trend and seasonality of the time series and found cointegration between the series. We included effects of the earthquake and built a segmented regression model to describe the time series finding that day-to-day changes in sentiment can either lead or lag daily changed mobility patterns. This study contributes a new lens to assess the dynamic process of disaster resilience unfolding over large spatial scales.

Typhoon disaster assessment based on Dombi hesitant fuzzy information aggregation operators

Abstract: Hesitant fuzzy set is an extension of the traditional fuzzy set, and it has the membership function which was expressed by several possible numbers. Since it was introduced, it has been received wide attention from scholars due to its powerful ability in describing the uncertainty. In this paper, we first introduce some new operations on hesitant fuzzy elements (HFEs) based on Dombi t-conorm and t-norm and then proposed some new aggregation operators for HFEs, such as hesitant fuzzy Dombi weighted averaging, hesitant fuzzy Dombi ordered weighted averaging, hesitant fuzzy Dombi weighted geometric, hesitant fuzzy Dombi ordered weighted geometric, hesitant fuzzy Dombi hybrid averaging and hesitant fuzzy Dombi hybrid geometric operator. Finally, a multiple attribute group decision-making approach under hesitant fuzzy environment is presented based on these proposed operators. A real example about typhoon disaster assessment is presented to show the advantages of the proposed method.

Effects of Particle Size of Mono-disperse Granular Flows Impacting A Rigid Barrier

Abstract: Understanding the interaction between complex geophysical flows and barriers remains a critical challenge for protecting infrastructure in mountainous regions. The scientific challenge lies in understanding how grain stresses in complex geophysical flows become manifested in the dynamic response of a rigid barrier. A series of physical flume tests were conducted to investigate the influence of varying the particle diameter of mono-dispersed flows on the impact kinematics of a model rigid barrier. Particle sizes of 3, 10, 23 and 38 mm were investigated. Physical tests results were then used to calibrate a discrete element model for carrying out numerical back-analyses. Results reveal that aside from considering bulk characteristics of the flow, such as the average velocity and bulk density, the impact load strongly depends on the particle size. The particle size influences the degree of grain inertial stresses which become manifested as sharp impulses in the dynamic response of a rigid barrier. Impact models that only consider a single impulse using the equation of elastic collision warrant caution as a cluster of coarse grains induce numerous impulses that can exceed current design recommendations by several orders of magnitude. Although these impulses are transient, they may induce local strucutral damage. Furthermore, the equation of elastic collision should be adopted when the normalized particle size with the flow depth, δ/h, is larger than 0.9 for Froude numbers less than 3.5.

Women’s empowerment following disaster: A longitudinal study of social change

Abstract: This paper examines changes in gender relations in a small coastal community as a result of the 2010 Chile earthquake and tsunami. Vulnerability and resilience are used as a conceptual framework to analyse these changes. Based on empirical evidence from a seven-year longitudinal study and quasi-ethnographic work, we explore changes in power relations at the different stages of the disaster and longer-term recovery as well as the conditions that fostered these changes. Our findings show distinct patterns of change. First, disasters can trigger long-lasting changes that challenge historical patriarchal relations. Second, while vulnerability increases following a disaster, resilience can potentially counteract women’s vulnerability. We propose that resilience can be a pathway to produce long-term changes in gender relations and empower women in the context of disasters.

Evaluation of CHIRPS and its application for drought monitoring over the Haihe River Basin, China

Abstract: Climate Hazards Group Infrared Precipitation with Stations data (CHIRPS) rainfall dataset was early evaluated and compared with 29 meteorological stations over the Haihe River basin in China, for the period 1981–2015. Seven statistical and categorical metrics were applied to evaluate the performance of CHIRPS with gauge measurements at multi-time scales (monthly, seasonally and annually). Using the Standardized Precipitation Index (SPI) as the drought indicator, the applicability of this new long-term satellite precipitation product for drought monitoring was investigated in this study. Results indicate that the good performances were performed at multiple temporal scales (monthly, seasonally and annually). Although it tends to overestimate the higher precipitation in this region, CHIRPS demonstrated good agreement (R2 > 0.70) with gauge observations at monthly scale and greater agreements (R2 > 0.78) at seasonal and annual scales. Meanwhile, CHIRPS performed a good score of BIAS and lower error in a majority of months at multi-time scales. Because of its good performance at multi-time scales and the advantages of high spatial resolution and long-time record, CHIRPS was applied to derive the SPI over the Haihe River basin. It is evaluated and compared with stations observations to derive SPI at time scale of 1, 3 and 6 months. The results indicate that it performed good ability to monitor drought (R2 > 0.70) and successfully captured the historical drought years (1981, 1999, 2001 and 2012). Overall, this study concludes that CHIRPS can be a valuable complement to gauge precipitation data for estimating precipitation and drought monitoring in this region.

Assessing flood hazard using flood marks and analytic hierarchy process approach: a case study for the 2013 flood event in Quang Nam, Vietnam

Abstract: The production of flood hazard assessment maps is an important component of flood risk assessment. This study analyses flood hazard using flood mark data. The chosen case study is the 2013 flood event in Quang Nam, Vietnam. The impacts of this event included 17 deaths, 230 injuries, 91,739 flooded properties, 11,530 ha of submerged and damaged agricultural land, 85,080 animals killed and widespread damage to roads, canals, dykes and embankments. The flood mark data include flood depth and flood duration. Analytic hierarchy process method is used to assess the criteria and sub-criteria of the flood hazard. The weights of criteria and sub-criteria are generated based on the judgements of decision-makers using this method. This assessment is combined into a single map using weighted linear combination, integrated with GIS to produce a flood hazard map. Previous research has usually not considered flood duration in flood hazard assessment maps. This factor has a rather strong influence on the livelihood of local communities in Quang Nam, with most agricultural land within the floodplain. A more comprehensive flood hazard assessment mapping process, with the additional consideration of flood duration, can make a significant contribution to flood risk management activities in Vietnam.

Socioeconomic vulnerability and electric power restoration timelines in Florida: the case of Hurricane Irma

Abstract: Large-scale damage to the power infrastructure from hurricanes and high-wind events can have devastating ripple effects on infrastructure, the broader economy, households, communities, and regions. Using Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates between urban and rural counties; (2) the duration of electric power outages in counties exposed to tropical storm force winds versus hurricane Category 1 force winds; and (3) the relationship between the duration of power outage and socioeconomic vulnerability. We used power outage data for the period September 9, 2017–September 29, 2017. At the peak of the power outages following Hurricane Irma, over 36% of all accounts in Florida were without electricity. We found that the rural counties, predominantly served by rural electric cooperatives and municipally owned utilities, experienced longer power outages and much slower and uneven restoration times. Results of three spatial lag models show that large percentages of customers served by rural electric cooperatives and municipally owned utilities were a strong predictor of the duration of extended power outages. There was also a strong positive association across all three models between power outage duration and urban/rural county designation. Finally, there is positive spatial dependence between power outages and several social vulnerability indicators. Three socioeconomic variables found to be statistically significant highlight three different aspects of vulnerability to power outages: minority groups, population with sensory, physical and mental disability, and economic vulnerability expressed as unemployment rate. The findings from our study have broader planning and policy relevance beyond our case study area, and highlight the need for additional research to deepen our understanding of how power restoration after hurricanes contributes to and is impacted by the socioeconomic vulnerabilities of communities.

Integrated methodology for flood risk assessment and application in urban communities of Pakistan

Abstract: Flood disasters and its consequent damages are on the rise globally. Pakistan has been experiencing an increase in flood frequency and severity along with resultant damages in the past. In addition to the regular practices of loss and damage estimation, current focus is on risk assessment of hazard-prone communities. Risk measurement is complex as scholars engaged in disaster science and management use different quantitative models with diverse interpretations. This study tries to provide clarity in conceptualizing disaster risk and proposes a risk assessment methodology with constituent components such as hazard, vulnerability (exposure and sensitivity) and coping/adaptive capacity. Three communities from different urban centers in Pakistan have been selected based on high flood frequency and intensity. A primary survey was conducted in selected urban communities to capture data on a number of variables relating to flood hazard, vulnerability and capacity to compute flood risk index. Households were categorized into different risk levels, such as can manage risk, can survive and cope, and cannot cope. It was found that risk levels varied significantly across the households of the three communities. Metropolitan city was found to be highly vulnerable as compared to smaller cities due to weak capacity. Households living in medium town had devised coping mechanisms to manage risk. The proposed methodology is tested and found operational for risk assessment of flood-prone areas and communities irrespective of locations and countries.

A review of advances in China’s flash flood early-warning system

Abstract: This paper summarizes the main flash flood early-warning systems of America, Europe, Japan, and Taiwan China and discusses their advantages and disadvantages. The latest development in flash flood prevention is also presented. China’s flash flood prevention system involves three stages. Herein, the warning methods and achievements in the first two stages are introduced in detail. Based on the worldwide experience of flash flood early-warning systems, the general research idea of the third stage is proposed from the viewpoint of requirements for flash flood prevention and construction progress of the next stage in China. Real-time dynamic warning systems can be applied to the early-warning platform at four levels (central level, provincial level, municipal level, and county level) . Through this, soil moisture, peak flow, and water level can be calculated in real-time using distributed hydrological models, and then flash flood warning indexes can be computed based on defined thresholds of runoff and water level. A compound warning index (CWI) can be applied to regions where rainfall and water level are measured by simple equipment. In this manner, flash-flood-related factors such as rainfall intensity and antecedent and cumulative rainfall depths can be determined using the CWI method. The proposed methodology for the third stage could support flash flood prevention measures in the 13th 5-Year Plan for Economic and Social Development of the People’s Republic of China (2016–2020). The research achievements will serve as a guidance for flash flood monitoring and warning as well as flood warning in medium and small rivers.

Research on carbon market price mechanism and influencing factors: a literature review

Abstract: Carbon emissions trading is being used by more and more countries or regions to solve the global warming problem. The establishment of China’s carbon market mechanism is still under exploration and improvement. This paper focuses on the price determination mechanism in the carbon market. Based on the price theories, we analyze the theoretical basis of the carbon price formation and the carbon price transmission mechanism from the perspective of the agents that affect carbon price. From these angles including residents’ demands, enterprises’ actual emissions and indirect effects on residents’ demands, the government’s setting for carbon market institutions and indirect effects on residents and enterprises, as well as energy markets and financial markets, we analyze how these factors influence the carbon price. In turn, we discuss how carbon price affects the enterprise costs, energy-saving technologies and residents’ welfare. Besides, we summarize the current price mechanism of domestic and overseas major carbon markets. Finally, based on the current research on carbon price theory and its influencing factors, we also present some further directions on carbon price mechanism and influencing factors including China’s carbon market price mechanism design, the quantitative analysis of carbon price factors and improvement of carbon price theory.

Rethinking flood risk communication

Abstract: Flooding is a serious hazard across Europe, with over 200 major floods documented in the last two decades. Over this period, flood management has evolved, with a greater responsibility now placed on at-risk communities to understand their risk and take protective action to develop flood resilience. Consequently, communicating flood risk has become an increasingly central part of developing flood resilience. However, research suggests that current risk communications have not resulted in the intended increase in awareness, or behavioural change. This paper explores how current risk communications are used by those at risk, what information users desire and how best this should be presented. We explore these questions through a multi-method participatory experiment, working together with a competency group of local participants in the town of Corbridge, Northumberland, the UK. Our research demonstrates that current risk communications fail to meet user needs for information in the period before a flood event, leaving users unsure of what will happen, or how best to respond. We show that participants want information on when and how a flooding may occur (flood dynamics), so that they can understand their risk and feel in control of their decisions on how to respond. We also present four prototypes which translate these information needs into new approaches to communicating flood risk. Developed by the research participants, these proposals meet their information needs, increase their flood literacy and develop their response capacity. The findings of the research have implications for how we design and develop future flood communications, but also for how we envisage the role of flood communications in developing resilience at a community level.

The link between social capital and disaster recovery: evidence from coastal communities in Bangladesh

Abstract: The purpose of this research is to explore the role social capital played in disaster coping and the recovery process among the southwest coastal villages of Bangladesh. Qualitative methods of data collection such as observation, semi-structured interviews and focus group discussions were carried out with individuals from several occupational groups in the two of most affected villages by cyclone Aila. The findings suggest that social capital played an instrumental role in personal, household and community recovery processes in the wake of the cyclone. In particular, the bonding and bridging social capital significantly helped the villagers from the emergency period to long-term recovery, while the benefits of linking social capital were reaped by only few individuals. It also shows that features of prevailing social structure—patronage networks and class hierarchy—paved the way to the misappropriation of the sizable amount of disaster relief and rehabilitation resources by local elites and to the channeling to the less affected households. This led to the erosion of bridging social capital of communities, and the reinforcement of unscrupulous linking social networks. These findings contribute both to the social capital literature as well as to formulating sustainable policy and programs for future disasters.

Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey

Abstract: The aim of this study is to make a comparison of the performances of two machine-learning algorithms that support vector machine (SVM) and random forest (RF) for landslide susceptibility mapping. The study makes use of a sampling strategy called two-level random sampling (2LRS). During landslide susceptibility mapping, training and testing samples must be collected from different landslide seed cells, which are then put through a fully independent sampling using the 2LRS algorithm. This approach requires fewer samples for the improvement of the computation time of both machine-learning classifications. The proposed approach was tested in the Alakir catchment area (Western Antalya, Turkey) which features numerous active deep-seated rotational landslides. In order to compare the performance of the machine-learning algorithms, three random sets were generated for SVM and three random sets generated for 10, 100, 1000 and 10,000-tree size RF. A total of 15 models were generated for comparison, and their spatial performances were performed by the area under the receiver-operating characteristic curves, which ranged between 0.82 and 0.87. The highest and lowest performances were recorded from two models in SVM and two models from the 1000-tree and 10,000-tree sized RF, respectively. These results were confirmed the landslide happened just after producing the susceptibility maps in the field.

Future changes in climate extremes over Equatorial East Africa based on CMIP5 multimodel ensemble

Abstract: This study investigates the variability of extreme rainfall (temperature) events in the twenty-first century based on 18 (24)-member multimodel simulations of models participating in phase 5 of the Couple Model Intercomparison Project (CMIP5). The study employed extreme indices defined by the WMO’s Experts Team on Climate Change Detection Indices, under two radiative forcing scenarios: RCP4.5 and RCP8.5. Two 30-year time periods, mid- (2021–2050) and end (2071–2100) of the twenty-first century, are considered for investigation of extremes, relative to the baseline period (1961–1990). Mann–Kendall test statistic and Sen’s slope estimator are used to investigate trend. Temperature shows a remarkable increase with an increase in radiative forcing. A sharp augmentation in temperature is projected towards the end of the twenty-first century. There will be almost zero cool days and cold nights by the end of the century. Very wet and extremely very wet days increase, especially over Uganda and western Kenya. Variation in maximum 1-day precipitation (R × 1 day) and maximum 5-day precipitation amount shows a remarkable increase in variance towards the end of the twenty-first century. Although the results are based on relatively coarse resolution data, they give likely conditions that can be utilized in long-term planning and be relied on in advanced studies.

Integration of stress testing with graph theory to assess the resilience of urban road networks under seismic hazards

Abstract: Transportation networks daily provide accessibility and crucial services to societies. However, they must also maintain an acceptable level of service to critical infrastructures in the case of disruptions, especially during natural disasters. We have developed a method for assessing the resilience of transportation network topology when exposed to environmental hazards. This approach integrates graph theory with stress testing methodology and involves five basic steps: (1) establishment of a scenario set that covers a range of seismic damage potential in the network, (2) assessment of resilience using various graph-based metrics, (3) topology-based simulations, (4) evaluation of changes in graph-based metrics, and (5) examination of resilience in terms of spatial distribution of critical nodes and the entire network topology. Our case study was from the city of Kathmandu in Nepal, where the earthquake on April 25, 2015, followed by a major aftershock on May 12, 2015, led to numerous casualties and caused significant damage. Therefore, it is a good example for demonstrating and validating the developed methodology. The results presented here indicate that the proposed approach is quite efficient and accurate in assisting stakeholders when evaluating the resilience of transportation networks based on their topology.

Investigation into geohazards during urbanization process of Xi’an, China

Abstract: Xi’an is the political, cultural and economic center in Northwestern China, and the demands for urbanization are growing dramatically in the past decades. During the rapid urbanization in Xi’an, ground fissure and land subsidence have been regarded as the two striking geohazards. At present, a total of fourteen ground fissures have been detected in Xi’an, among which eight ground fissures have a high level of activity, while the other six ground fissures are of lowly active. Several land subsidence funnels appear in different regions of Xi’an, and the annual land subsidence shows a decreasing tendency after 1991, which is estimated to be around 40 mm/year in recent years. The reasons triggering geohazards can be divided as: (1) natural factors and (2) anthropogenic factors. Analysis of the countermeasures against the prevention and mitigation of geohazards indicates that public awareness is an important issue to a success of the geoenvironment protection. In addition, the existing monitoring technologies (GPS, InSAR, and GIS) together with the technical improvement in other fields are deemed to be necessary for an effective monitoring and mitigation of the geohazards.