Types Of Extreme Events Research Articles

Contributions of natural climate variability on the trends of seasonal precipitation extremes over China

AbstractThe increased trends of extreme precipitation events over China have been partially attributed to global warming, while it is unclear how the natural climate variability affect these variations in extreme rainfall trends. Here we examine the contributions of large‐scale modes climate variability from the Pacific Ocean, which are represented by El Niño‐Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), on the trends of seasonal extreme events across China. Results show that the effects of climate variability modes on precipitation extremes exhibit evidently spatiotemporal characteristics at the seasonal scale. A significant positive trend is found over southeastern and northwestern China in four seasons, and substantial increases in two types of extreme events are also seen over northwestern China in spring and winter. Most of the seasonal trends of extreme precipitation events can be explained by the trends of the principal components. El Niño (La Niña) and PDO–like sea surface temperature (SST) anomalies over the Pacific have a close linkage with the variations in precipitation extremes. By modulating the large‐scale atmospheric circulations, these Pacific SST anomalies associated with climatic oscillations, such as ENSO and PDO, provide the conducive conditions responsible for the occurrences of seasonal precipitation extremes across China. The seasonal trends of extreme events can be largely explained by the variability of the ENSO and PDO episodes, with a relatively larger modulation of PDO as compared to ENSO. These researches have important implications for improving seasonal flood disaster predictability and climate model performances.

Intensified impacts on mortality due to compound winter extremes in the Czech Republic

Although impacts of extremely cold temperatures on human health have been widely studied, adverse effects of other extreme weather phenomena have so far received much less attention. We employed a high-quality long-term mortality time series (1982–2017) to evaluate impacts of extreme winter weather in the Czech Republic. We aimed to clarify whether compound events of extreme weather cause larger impacts on mortality than do each type of extreme if evaluated individually. Using daily data from the E-OBS and ERA5 datasets, we analyzed 9 types of extreme events: extreme wind gust, precipitation, snowfall, and sudden temperature and pressure changes. Relative mortality deviations from the adjusted baseline were used to estimate the immediate effect of the selected extreme events on excess mortality. The impact was adjusted for the effect of extreme cold.Extreme events associated with sudden rise of minimum temperature and pressure drops had generally significant impact on excess mortality (3.7% and 1.4% increase). The impacts were even more pronounced if these events occurred simultaneously or were compounded with other types of extremes, such as heavy precipitation, snowfall, maximum temperature rise, and their combinations (increase as great as 14.4%). Effects of some compound events were significant even for combinations of extremes having no significant impact on mortality when evaluated separately. On the other hand, a “protective” effect of pressure increases reduced the risk for its compound events. Meteorological patterns during extreme events linked to excess mortality indicate passage of a low-pressure system northerly from the study domain. We identified extreme winter weather events other than cold temperatures with significant impact on excess mortality. Our results suggest that occurrence of compound extreme events strengthen the impacts on mortality and therefore analysis of multiple meteorological parameters is a useful approach in defining adverse weather conditions.

Data Science for Weather Impacts on Crop Yield

Private businesses in sectors such as food, energy and retail, as well as public sector and federal agencies are interested in the predictive understanding of weather impacts on crop yield, which is an important aspect of food security. Scientific literature has mainly examined how crop yield is impacted by growing season averaged weather indices. Although a few studies did consider weather extremes in their analysis, their scope was either restricted to measuring their conditional relationship with yield or the extreme event types considered were limited. Selection of regression models, whether the more commonly used linear approaches or nonlinear methods, have not been appropriately justified in this context. Here, we develop data-driven methods to examine two inter-related hypotheses for improved scientific understanding and enhanced predictive modeling. The first hypothesis, that extreme weather indices have a statistically significant information content in them is found to be valid based on linear and nonlinear methods for pairwise dependence. The second hypothesis, examines the value addition of nonlinear regression methods, and suggests that linear approaches may not alone be adequate. The results of this study can inform scientific understanding, generation and relevance of indices and end-to-end risk assessment systems in the context of climate impacts on crop yield. An immediate application may be in the context of NASA Earth Exchange (NEX) which facilitates the generation and dissemination of impacts relevant weather data and indices using a multitude of satellite-derived data sets and model outputs.

Hazards of extreme events in China under different global warming targets

ABSTRACT Changes in the frequency and intensity of extreme events affect the risk pattern of risk-bearing bodies and the effectiveness of response measures. Based on future climate scenario data, in this study, we identify global warming periods of 1.5°C and 2°C above pre-industrial levels, and the temporal and spatial patterns of temperature and precipitation in China during the same period, propose a methodology for risk assessment and integrated analysis of extreme events, build a database of extreme events and their hazards in China in the context of climate change, and assess the hazards caused by extreme events (i.e., heatwaves, floods, and droughts) in China under different warming periods. Under the RCP4.5 and RCP8.5 scenarios, global temperatures increase by 1.5°C and 2°C above pre-industrial levels by 2029 and 2026 and by 2050 and 2040, respectively. The warming would be fast in the north and slow in the south, the warming of the Qinghai-Tibet Plateau would accelerate, and the precipitation would increase in the north and decrease in the south, but the trend of the precipitation reduction in the south would be more significant under the RCP8.5 scenario. For 2°C of warming, the hazard index of severe heatwaves in the North China Plain, parts of South China, and the western part of Inner Mongolia would double that of 1.5°C warming. Regional differences in extreme precipitation would increase, especially under the RCP8.5 scenario, the hazard index of severe floods in parts of East China would be about twice that of 1.5°C warming. Under the joint influence of rising temperature and changing precipitation, the drought situation would be alleviated under the RCP4.5 scenario, but it is intensified under the RCP8.5 scenario. For 2°C of warming, the hazard index of severe droughts in some areas would reach three times that of 1.5°C warming. For the integrated hazard of the three types of extreme events, the areas of high and medium hazards would expand, while the areas of low hazard would decrease. For 2°C of warming, the areas of high and medium hazards would be more than half the total area.

High flow variability and storm events shape the ecology of the Mbhashe Estuary, South Africa

The possible impacts of extreme events on the ecology of selected aquatic biota within the Mbhashe Estuary were investigated during a four year (2010–2013) spring sampling programme. During periods of low to average flow conditions the estuary is shallow, turbid and characterised by the presence of fluid mud and the build-up of mud and clay deposits. During these conditions, extremely high biomasses of intertidal microalgae and zooplankton are present. Fish and macroinvertebrate abundance and diversity are also highest during low-flow conditions. Flood events can reset, or partially disrupt, the sediment erosion/depositional cycle and decrease the biomass and diversity of plankton, fish and macroinvertebrate species. The Mbhashe Estuary’s unique fluid mud habitat is therefore subjected to regular resetting, which potentially contributes to the fluid nature of the muds. A storm surge in 2011 resulted in the temporary dieback of an area of mangroves, as a result of the deposition of marine sediment. Although extreme events seem to play an important role in the deposition and erosion cycle of the estuary, an increased frequency of both types of extreme events may ultimately result in estuarine habitat loss, which will adversely affect the biota of the estuary.

A coordinated planner-disaster-risk-averse-planner investment model for enhancing the resilience of integrated electric power and natural gas networks

Nowadays, natural hazards and extreme events bring about significant losses in various infrastructures including integrated electric power and natural gas networks. Thus, a robust approach is necessary for investment and operation planning of integrated energy systems. This paper presents a novel tri-level planner-disaster-risk-averse-planner framework for investment planning in integrated electric power and natural gas networks (PDP-PEN) under extreme events. The notion of the information-gap decision theory (IGDT) as a proficient non-deterministic optimization technique under severe uncertainty is utilized to enhance the resilience of the integrated system under different types of extreme events in electric power and natural gas networks through a specific parameter named as the budget of resilience. The proposed PDP-PEN framework is solved by means of a tri-level algorithm including (1) first-level: planning problem under normal condition, (2) second-level: resilience evaluation under disastrous condition, and (3) third-level: risk-averse re-planning under disastrous condition using the notion of IGDT. This resilience-oriented investment planning tool is successfully implemented on the integrated Garver 6-node electric power and 8-node natural gas networks as well as the integrated IEEE 24-node electric power and 12-node natural gas networks. Case studies justify the effectiveness of the proposed IGDT-based tri-level approach to enhance the resilience of the integrated energy systems in response to extreme events.

Reactions to News and Reasoning By Exemplars

The presence of both systematic under-and-overreaction to news in financial markets is a major puzzle. We propose a systematic predictor of under-and-overreaction to news: the extremeness of the associated distribution of fundamentals. Using a comprehensive database of corporate news events, we identify substantial heterogeneity in both reactions to news and extremeness of fundamentals across types of corporate events. We document overreaction to more extreme event-types, such as leadership changes, M&A, and customer announcements, and underreaction to less extreme event-types such as earnings announcements. We show this is consistent with diagnostic expectations, a model of belief formation based on the representativeness heuristic. The model further predicts greater trading volume holding fixed fundamentals and more sensitive belief changes to more extreme corporate events, which we confirm in the data. We calibrate our model and show that it quantitatively matches the key features in our data.

Water Circulation Off the Northeastern Coast of Sakhalin during the Passage of Three Types of Deep Cyclones over the Sea of Okhotsk

Based on the numerical simulation of water circulation in the Sea of Okhotsk in 1986 to 2015, the impact of deep cyclones on the circulation off the northeastern coast of Sakhalin is studied. The circulation in the Sea of Okhotsk is simulated with the COSMO-Ru-INMOM-CICE model configuration, where the COSMO-Ru and INMOM resolve explicitly the mesoscale atmosphere and ocean dynamics and the CICE resolves the ice cover evolution. The extreme atmospheric events associated with the intensive cyclone activity over the Sea of Okhotsk during the cold season are classified. It is found that high velocity is typical of the cyclones coming to the sea from Sakhalin, and wind speed on the periphery is higher for the cyclones coming to the Sea of Okhotsk from the south and southwest. The analysis of water circulation response off the northeastern coast of Sakhalin demonstrates that the meridional current velocity on the shelf increased by several times from the sea surface to the bottom for all types of cyclones. On the edge of the shelf, southern currents intensified in the surface and bottom layers during the passage of cyclones and at the intermediate depths during the passage of fronts. On the continental slope, southern currents intensified in the surface, intermediate, and bottom layers depending on the type of extreme events.

‘Is global warming affecting the weather?’ Evidence for increased attribution beliefs among coastal versus inland US residents

ABSTRACTIncreasingly, researchers studying public beliefs about global warming have turned to the question of whether individuals have begun to perceive changes to their local climate conditions and to what extent they attribute these changes to the phenomenon of global warming. Perceptions of particular types of extreme events, i.e. extreme heat and droughts, have attracted the most attention, whereas the possible effects of place (such as proximity to coastal areas vulnerable to sea-level rise) on public beliefs about the link between changes to weather patterns and global warming have been largely neglected. This study matches geo-located responses to a nationally representative survey of US residents with climate extremes data in order to investigate the social and physical factors shaping public views about the links between global warming and extreme weather. Specifically, regional-level Climate Extremes Indices (CEI) are modelled together with individual-level socio-demographic characteristics and an indicator of coastal residence to test whether the incidence of extreme events and proximity to the coasts, net of social and economic factors, correspond to increased perceptions that global warming has affected the weather. Results indicate that coastal shoreline county residence significantly predicts individuals’ beliefs about the extent to which global warming is affecting the weather.

Importance of Framing for Extreme Event Attribution: The Role of Spatial and Temporal Scales

Event attribution, which determines how anthropogenic climate change has affected the likelihood of certain types of extreme events, is of broad interest to industries, governments, and the public. Attribution results can be highly dependent on the definition of the event and the characteristics assessed, which are part of framing the attribution question. Despite a widely acknowledged sensitivity to framing, little work has been done to document the impacts on attribution and the resulting implications. Here, we use a perfect‐model approach and large ensembles of coupled climate‐model simulations to demonstrate how event attribution depends on the spatial and temporal scales used to define the event. In general, stronger attribution is found for events defined over longer time scales and larger spatial scales due to enhanced signal‐to‐noise ratios. With strong warming trends, most regions see large changes in the likelihood of temperature extremes at all scales, even at low levels of global mean temperature increase. For precipitation extremes, spatial scale plays a strong role. It may be possible to attribute changes in likelihood for extreme precipitation events defined over larger scales, but greater levels of global warming are often required before it is possible to attribute changes in the likelihood of smaller‐scale precipitation events. Care must be taken to understand the scales used in event attribution, in order to properly understand the results.

Evaluation of synoptic‐scale patterns during extreme temperature and precipitation events in Alaska

Synoptic‐scale patterns associated with daily temperature and precipitation extremes in Alaska are identified and evaluated for daily variability in order to understand consistency in forcing mechanisms associated with extreme events as well as the tendency for each pattern to produce an extreme event. Daily station data at five locations for the 29‐year period from 1982 to 2010 are used. The widely recognized ClimDex indices are used to identify extreme high temperature, low temperature, and single‐day precipitation events. Pressure patterns during extreme events are evaluated seasonally for summer (JJA) and winter (DJF) at mean sea level pressure, 700 and 500‐hPa geopotential heights. Temperature extremes are largely characterized by synoptic‐scale patterns associated with substantial warm and cold air advection, and precipitation extremes are associated with moisture advection from the North Pacific. Extreme warm and cold temperature events are generally associated with more consistent daily synoptic‐scale patterns than precipitation. Considerable dissimilarities among daily synoptic‐scale patterns associated with extreme events based on location, type of extreme event and season highlight the need for careful consideration in the practice of applying synoptic‐scale forcing patterns to forecast or downscale extreme weather events in Alaska.

A Common Yardstick to Measure the Effects of Different Extreme Climatic Events on Soil Arthropod Community Composition Using Time-Series Data

Extreme climatic events can have profound effects on ecosystems. Climate change is causing an increase in the frequency and intensity of extreme events, which raises the probability that natural ecosystems will be exposed to subsequent or simultaneous extreme events. Exposure to multiple extreme events may involve very different abiotic stressors, which makes it hard to compare their consequences for more than a single or a few species, limiting our knowledge of community level effects. Here, we propose a novel approach to assess how different abiotic stressors influenced soil arthropod communities in a coastal ecosystem, using a species abundance time series of more than two years. We first correlated shifts in community composition with the climatic conditions preceding the sampling dates. Temperature was found to be the most important factor influencing community composition in both locations. Based on local meteorological data, we then defined five types of extreme events which occurred during our monitoring period: heat, cold, drought, heavy precipitation and high sea water levels. To compare the effect of different types of extreme climatic events on community composition, we calculated the multidimensional Hellinger distance between two subsequent sampling dates, which is a measure for the compositional dissimilarity between communities. Extreme events were expected to result in a larger change in community composition between sampling dates and thus a larger Hellinger distance. However, no significant difference in Hellinger distance was found for intervals with or without extreme events in three out of the four location-vegetation combinations. In the saline location with an open vegetation type we found that Hellinger’s distance was reduced when extreme events had occurred, which is discussed in the light of the buffering potential of different vegetation types. Our study illustrates the novel use of an established method from the community ecological toolbox to facilitate direct comparison of different types of extreme climatic events on community composition. We highly encourage other ecologists with long-term monitoring datasets to perform similar analyses and test the general applicability of this method.

Climate disaster, gender, and violence: Men's infliction of harm upon women in the Philippines and Vietnam

The intensity and frequency of extreme weather and climate events are expected to increase due to anthropogenic climate change. This systematic review explores extreme events and their effect on gender-based violence (GBV) experienced by women, girls, and sexual and gender minorities. We searched ten databases until February, 2022. Grey literature was searched using the websites of key organisations working on GBV and Google. Quantitative studies were described narratively, whereas qualitative studies underwent thematic analysis. We identified 26 381 manuscripts. 41 studies were included exploring several types of extreme events (ie, storms, floods, droughts, heatwaves, and wildfires) and GBV (eg, sexual violence and harassment, physical violence, witch killing, early or forced marriage, and emotional violence). Studies were predominantly cross-sectional. Although most qualitative studies were of reasonable quality, most quantitative studies were of poor quality. Only one study included sexual and gender minorities. Most studies showed an increase in one or several GBV forms during or after extreme events, often related to economic instability, food insecurity, mental stress, disrupted infrastructure, increased exposure to men, tradition, and exacerbated gender inequality. These findings could have important implications for sexual-transformative and gender-transformative interventions, policies, and implementation. High-quality evidence from large, ethnographically diverse cohorts is essential to explore the effects and driving factors of GBV during and after extreme events.

Extreme events in systems with discontinuous boundaries

We describe a class of extreme events emerging in systems with discontinuous boundaries that exhibit stick-slip dynamics. This kind of systems is capable of generating extreme events when the system trajectory sticks to a sliding set and slides for a relatively large distance along the discontinuous boundary to one of the system's subspaces. We present two examples of such systems, a microelectromechanical cantilever and a driven class-B laser, where this type of extreme events appear. We show that their forecasting is possible by monitoring the sliding trajectory along the discontinuous boundary. In both cases, the probability distribution of recurrence times exhibits a power-law behavior, typical for extreme events. The results of this study can be of interest for engineering applications, for example, to predict extremely large-amplitude oscillations in cantilevers, systems with friction, lasers, and other systems with discontinuous boundaries.

Large-Scale Environmental Conditions Related to Midsummer Extreme Rainfall Events around Japan in the TRMM Region

The precipitation characteristics of extreme events in August determined from 13 years of satellite data around Japan in the TRMM observation region and their relationship with large-scale environmental conditions are examined. Two types of extreme events, extreme rainfall and extreme convective events, are defined in each analysis grid box using maximum near-surface rainfall and maximum 40-dB Z echo-top height in each event, respectively. There are clear differences in precipitation characteristics between the two types of extreme events. Extreme rainfall events are more organized precipitation systems than the extreme convective events, with relatively lower echo-top heights and very low lightning activity. There are also clear differences in the related environmental conditions, where the environments related to the extreme rainfall events are somewhat convectively stable and very humid in almost the entire troposphere. These facts are consistent with our previous studies and reinforce the importance of warm-rain processes in extremely intense precipitation productions. The environments related to the extreme rainfall events exhibit a zonally extended moist anomaly in the free troposphere from southern China to the east of Japan, indicating that the excessive moisture transported from the west by a large-scale flow may partially play a role in producing environmental conditions favorable for extreme rainfall. On the other hand, the environments related to extreme convective events are not associated with free-tropospheric moisture inflow. The relationships with the tropical cyclones and upper-tropospheric dynamical fields are also examined, and are found to be clearly different between the extreme rainfall events and extreme convective events.

Modelling the occurrence of heat waves in maximum and minimum temperatures over Spain and projections for the period 2031-60

Abstract The occurrence of extreme heat events in maximum and minimum daily temperatures is modelled using a non-homogeneous common Poisson shock process. It is applied to five Spanish locations, representative of the most common climates over the Iberian Peninsula. The model is based on an excess over threshold approach and distinguishes three types of extreme events: only in maximum temperature, only in minimum temperature and in both of them (simultaneous events). It takes into account the dependence between the occurrence of extreme events in both temperatures and its parameters are expressed as functions of time and temperature related covariates. The fitted models allow us to characterize the occurrence of extreme heat events and to compare their evolution in the different climates during the observed period. This model is also a useful tool for obtaining local projections of the occurrence rate of extreme heat events under climate change conditions, using the future downscaled temperature trajectories generated by Earth System Models. The projections for 2031-60 under scenarios RCP4.5, RCP6.0 and RCP8.5 are obtained and analysed using the trajectories from four earth system models which have successfully passed a preliminary control analysis. Different graphical tools and summary measures of the projected daily intensities are used to quantify the climate change on a local scale. A high increase in the occurrence of extreme heat events, mainly in July and August, is projected in all the locations, all types of event and in the three scenarios, although in 2051-60 the increase is higher under RCP8.5. However, relevant differences are found between the evolution in the different climates and the types of event, with a specially high increase in the simultaneous ones.

Projections of Twenty-First-Century Climate Extremes for Alaska via Dynamical Downscaling and Quantile Mapping

AbstractClimate change is expected to alter the frequencies and intensities of at least some types of extreme events. Although Alaska is already experiencing an amplified response to climate change, studies of extreme event occurrences have lagged those for other regions. Forced migration due to coastal erosion, failing infrastructure on thawing permafrost, more severe wildfire seasons, altered ocean chemistry, and an ever-shrinking season for snow and ice are among the most devastating effects, many of which are related to extreme climate events. This study uses regional dynamical downscaling with the Weather Research and Forecasting (WRF) Model to investigate projected twenty-first-century changes of daily maximum temperature, minimum temperature, and precipitation over Alaska. The forcing data used for the downscaling simulations include the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; 1981–2010), Geophysical Fluid Dynamics Laboratory Climate Model, version 3 (GFDL CM3), historical (1976–2005), and GFDL CM3 representative concentration pathway 8.5 (RCP8.5; 2006–2100). Observed trends of temperature and sea ice coverage in the Arctic are large, and the present trajectory of global emissions makes a continuation of these trends plausible. The future scenario is bias adjusted using a quantile-mapping procedure. Results indicate an asymmetric warming of climate extremes; namely, cold extremes rise fastest, and the greatest changes occur in winter. Maximum 1- and 5-day precipitation amounts are projected to increase by 53% and 50%, which is larger than the corresponding increases for the contiguous United States. When compared with the historical period, the shifts in temperature and precipitation indicate unprecedented heat and rainfall across Alaska during this century.

On the use of self‐organizing maps for studying climate extremes

AbstractUnderstanding how climate extremes are sensitive to a changing climate requires characterization of the physical mechanisms behind such events. For this purpose, the application of self‐organizing maps (SOMs) has become popular in the climate science literature. One potential drawback, though not unique to SOMs, is that the background synoptic conditions represented by SOMs may be too generalized to adequately describe the atypical conditions that can co‐occur during the extreme event being considered. In this paper, using the Australian region as a case study, we illustrate how the commonly used SOM training procedure can be readily modified to produce both more accurate patterns and patterns that would otherwise occur too rarely to be represented in the SOM. Even with these improvements, we illustrate that without careful treatment, the synoptic conditions that co‐occur during some types of extreme events (i.e., heavy rainfall and midlatitudinal cyclone occurrence days) risk being poorly represented by the SOM patterns. In contrast, we find that during Australian heat wave events the circulation is indeed well represented by the SOM patterns and that this application can provide additional insight to composite analysis. While these results should not necessarily discourage researchers seeking to apply SOMs to study climate extremes, they highlight the importance of first critically evaluating the features represented by the SOM. This study has provided a methodological framework for such an evaluation which is directly applicable to other weather typing procedures, regions, and types of extreme events.

Role of interactions between cloud microphysics, dynamics and aerosol in the heavy rainfall event of June 2013 over Uttarakhand, India

In this study, we propose a hypothesis, supported by numerical model simulations, concerning the role of cloud microphysical processes and aerosols in the invigoration of an extreme rainfall event over Uttarakhand in June 2013. The interactions among dynamics, thermodynamics and microphysical processes and their feedbacks play a vital role in the occurrence of extreme events. To test the proposed hypothesis, Weather Research and Forecasting (WRF) simulations are carried out with three different microphysical schemes (i.e. WDM6, Morrison, and CLR). The role of aerosol indirect effects in the process of invigoration of precipitation is demonstrated with a high‐resolution regional model for the extreme event over the foothills of the Himalayas. The extreme event is characterized by the strong north–south tropospheric temperature gradient and strong moisture convergence. Forced uplift beyond the freezing level initiates a precipitation process which involves cloud ice and mixed‐phase cloud microphysics and latent heat release; further, it invigorates convection and enhances precipitation. Results pinpoint that the role of microphysical processes are very crucial during such a type of extreme event. Additionally, the result accentuates the importance of aerosols on the deep convective cloud systems which have influence through invigoration and involvement of complex interactions between aerosol, large‐scale dynamics and cloud microphysics.

Network-Based Modeling for Characterizing Human Collective Behaviors During Extreme Events

Modeling and predicting human dynamic behaviors in the face of stress and uncertainty can help understand and prevent potential irrational behavior, such as panic buying or evacuations, in the wake of extreme events. However, in terms of the types of events and the distinct human psychological factors, such as risk perception (RP) and emotional intensity (EI), human dynamic behaviors exhibit heterogeneous spatiotemporal characteristics. For example, we can observe different collective responses to the same events by people in different regions, with distinct trends unfolding over time. To provide a computational means for understanding the spatiotemporal characteristics of human behaviors during different types of extreme events, here we present a network-based model that enables us to characterize dynamic behaviors. This model assumes the perspective of a dynamic system, whose behavior is driven by human psychological factors and by the network structure of interactions among individuals. By making use of the available data from Twitter and GoogleTrends, we conduct a case study of human dynamic behavioral and emotional responses to the Japanese earthquake in 2011 in order to examine the effectiveness of our proposed model. With this model, we further assess the impacts of an event by evaluating the interrelationships of human RP and levels of EI in terms of observed collective behaviors. The results demonstrate that human behaviors are subjected to personal observations, experiences, and interactions, which can potentially alter perceptions and magnify the impacts of an event.