Natural Hazards Research最新文献

West Bengal is situated primarily in the Surma Valley at the foothills of the Himalayas and near the western foreland of the Assam-Arakan Orogenic Belt. Several low to moderate-magnitude earthquakes are felt in the region frequently. In this study, we use integrated multi-criteria decision-making (MCDM) models to assess the seismic vulnerability in West Bengal. Twenty-four parameters that were susceptible to seismicity in the region have been used to evaluate geotechnical, structural, social, and physical vulnerability. The analytical hierarchy process (AHP) model was used to estimate the priorities of the parameters, which was then used to estimate seismic vulnerability using the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and the weighted sum method (WSM). The results reveal that approximately ∼17.81% of the total area and ∼65.36% population may fall under a high to very high-vulnerable zone, causing concerns for planning and disaster mitigation. The receiver operating characteristic curve estimated to validate the results, indicate that the AHP-VIKOR performs better for seismic vulnerability estimation. The results of this study may help various mitigation and planning agencies in identifying earthquake-vulnerable zones and preparing in advance for any potential large magnitude earthquakes that may occur in the region.

Improving disaster prevention, reduction, and emergency response capabilities is crucial in a country prone to frequent natural disasters. Since the release of ChatGPT, it has garnered widespread attention and sparked extensive discussions in various fields due to its powerful language processing and reasoning abilities. This paper explores the application of ChatGPT in natural disaster prevention and reduction, building upon its language capabilities. The paper examines ChatGPT's ability to gather information and its potential for disaster prevention science popularization and education. It describes the rapid response and availability of ChatGPT in natural disaster prevention and highlights its potential to assist emergency response efforts. The paper also outlines ChatGPT's assistance in the pre-disaster, during-disaster, and post-disaster phases. Additionally, it points out the current limitations and challenges in applying ChatGPT and provides prospects for future research directions in natural disaster prevention and reduction.

Due to global climate change, community resilience to natural disasters has become a high priority in environmental research. Academicians and practitioners from different disciplines and organizations include several dimensions to outline the process of building resilient communities. Although this research branch suffers from the lack of a shared theoretical and methodological consensus, many scholars publish research articles each year. Similarly, social scientists include diverse contextual humanitarian dimensions that are challenging to trace. Therefore, this study attempts to undertake a systematic review of the literature of the last 12 years (2010–2021) to outline the current trends in research methods, selected dimensions, and theoretical standpoints from the social perspective. This systematic observation of the literature identifies the recent trends in adopting research design, sampling design, and data collection techniques used for the research. The present review also traces the propensity of including major theoretical dimensions in the research. After identifying the contemporary trends in research, we find that a comprehensive multi-phase research model is necessary to initiate an effective policymaking in a specific socio-ecological context.

Following the 2018 Palu tsunami event in Central Sulawesi, Indonesia, the geological evidence of paleotsunami in Palu, was investigated to extend the historical record of past tsunamis in this area. A geological survey was conducted in Talise Beach, Palu City, at the southern end of Palu Bay. The stratigraphy sequence from the outcrop profile in Talise Beach showed three clear paleotsunamis. These paleotsunamis are distributed widely and can be traced along 1 ​km parallel to the beach, and each layer is characterized by poorly sorted sand deposited on the paleosoil. The boundary between the sand layers and paleosoils is sharp and erosional. The sedimentological and foraminifera analyses support the identification of paleotsunamis. Based on the radiocarbon dating, it was interpreted that these three paleotsunamis occurred in the 17th, 18th, and 19th century. This first paleotsunami study in Palu, not only provided geological evidence of paleotsunamis, and extended the tsunami record in Palu, but also served as an essential start for tsunami geology study in Palu as the tsunami sources in this area are complex.

This paper presents seismic hazard and risk assessment for the state of Mizoram based on a classical probabilistic seismic hazard analysis and event-based probabilistic seismic risk analysis. For the seismic hazard estimation, analysis has been performed considering the areal source model, fault zone polygon, and smoothed gridded seismicity model. The earthquake activity rates for these source zones and smoothed gridded seismicity sources are estimated from the homogenized and declustered earthquake catalogue. The logic tree framework is applied in the seismic source models and Ground Motion Prediction equations (GMPEs) to account for the epistemic uncertainties. Five Next-generation attenuation (NGA) GMPEs for the active shallow region and three GMPEs for the Indo-Burma subduction zone have been used to evaluate the hazard at the reference rock condition (Vs30 ​= ​760 ​m/s). Peak Ground Acceleration (PGA) and Spectral Acceleration (SA) at 0.2 s and 1s are estimated for each eight districts headquarters of Mizoram for a 2% and 10% probability of exceedance in 50 years. The hazard curves and Uniform Hazard Spectra (UHS) are also presented. For seismic risk analysis, building exposure data are based on digitized building footprint and 2011 Housing Census data of India. All the buildings are classified into three classes, and seismic vulnerability functions are assigned to each building class. The area per building class is assigned from the digitized footprint. Building replacement costs per square meter have been chosen based on expert input and values identified from past study. Lastly, the study conducted a seismic risk analysis using the Open Quake-engine's probabilistic event-based methodology to estimate risk metrics at the district level, such as average annual losses and probability curves for loss exceedance. The study's findings provide valuable insights into the most high-risk areas, the building construction types that are most vulnerable to seismic activity, and the anticipated economic losses in the state of Mizoram. These results can serve as a guide for local government authorities in developing future city plans and implementing earthquake risk mitigation strategies.

After the fall of Communism in the Czech Republic, the centralised flood management was entrusted to a municipality. The new approach to disaster management in 1997 emphasised on emergency preparedness at the local level. The changing paradigms of emergency management and the changing rules and regulations of insurance companies have increased the responsibility of the local government to reduce the loss of property and save lives in villages. A qualitative method was used to explore a phenomenon in a bounded system and to understand the role and responsibilities of the municipality and the villagers' expectations. In addition, policy papers were examined to help understand the emergency planning of the municipality. The study found that successful emergency leadership is associated with adequate planning, appropriate strategies and effective implementation. The leaders must encourage subordinates and other individuals to participate actively in emergency management. It has been observed that the mayor created a ‘sense of urgency’ after assessing the prevailing institutional situation so that the plans to manage the emergency may be implemented immediately. The municipality also improves its disaster management strategies by identifying the causes of failure in the past, rectifying existing gaps, building confidence among villagers and preventing migration. It is not expected that all the stakeholders, staffs and elected members at the grassroots level engage in emergency management activities equally. However, leadership of a mayor can bind all the stakeholders to achieve a successful emergency management. They may not have adequate emergency management knowledge, but the sharing of knowledge through workshops and training programmes enhance their skills. The effective emergency management at the grassroots level not only requires collaborative strategies and human resource management, but also needs adequate management of funds. Because the local government can not always depend on voluntary participation and contribution, in which the mayor plays an important role.

The present study focuses on developing a landslide susceptibility zonation (LSZ) using GIS-based bivariate statistical model in the Lunglei district of Mizoram. Initially, 17 factors were selected after calculating the multicollinearity test for LSZ. A landslide inventory map was created based on 234 historic landslide events, which were randomly divided into training (70%) and testing (30%) datasets. Using the Index of Entropy (IOE) model, nine causative factors were identified as having significant weightage for LSZ: elevation, slope, aspect, curvature, normalized difference vegetation index, geomorphology, distance to road, distance to lineament, and distance to river. On the other hand, factors such as land use land cover, stream power index, terrain ruggedness index, terrain roughness, topographic wetness index, annual rainfall, topographic position index, and geology had negligible weightage. Based on the relative importance of the causative factors, two models were developed: scenario 1, which considered nine factors, and scenario 2, which considered all 17 factors. The results revealed that 16% and 14% of the district area were identified as very highly landslide prone in scenario 1 and scenario 2, respectively. The high susceptibility zone accounted for 26% and 25% of the area in scenario 1 and scenario 2, respectively. To assess the accuracy of the models, a receiver operating characteristic (ROC) curve and quality sum ratio method was performed using 30% of the testing landslide data and an equal number of non-landslide data points. The area under the curve (AUC) for scenario 1 and scenario 2 were 0.947 and 0.922, respectively, indicating higher efficiency for scenario 1. The quality sum ratios were 0.435 and 0.43 for scenario 1 and scenario 2, respectively. Based on these results, the LSZ mapping from scenario 1 is considered suitable for policymakers to address development and risk reduction associated with landslides.

As tourism and its related sectors have flourished in Bali Province, Denpasar Municipality, as the capital, has attracted significant urbanization. As a result of this development tendency, the city has become the densest location in the Bali Area. Denpasar Municipality is suffering with urban issues such as waste, land-use changing, housing bubble, and cultural asset loss as a result of the negative effects of urbanization. Not only from the degradation of urban livelihood threat, but Denpasar is also at risk from multi-hazard disasters such as earthquakes, tsunami, floods, extreme weather, forest and land fire, extreme waves, and beach erosion. Currently, the outbreak of the COVID-19 pandemic, as well as the decline of the tourism business, have forced Denpasar Municipality's resilience to the edge. In addition, to address the threat of disaster and urban issues in Denpasar, this research was conducted to analyze the resilience in the city. Yet, the previous studies have not been addressed the resilience of the urban crisis and disaster in a holistic approach. First, the semi-qualitative research by CDRI (Climate Disaster Resilience Index) Framework was conducted to measure the urban resilience in Denpasar. The result of five parameters (physical, social, institutional, economic, and environment) reveals that West Denpasar has the highest resilience score, followed by South, North, and East Denpasar. In addition, to assess the supporting and restricting resilience factors in Denpasar, a qualitative approach using semi-structured interviews with different responsible institutions for disaster management in Denpasar was undertaken. The result shows that sufficient infrastructure and facilities, bonus demographic, collaboration with the private sector, sufficient information access, and control from the government are the supporting factors of resilience while urbanization challenge, budget shifting, the management of the problem, the ownership of the asset, collaboration with the community and focus on physical loss and damage are the restraining factors of resilience in Denpasar Municipality.

Monitoring drought characteristics is crucial for understanding drought behaviour and developing effective mitigation plans. In this study, we analyze the characteristics of meteorological droughts in the eastern Himalayan region by utilizing both the Standardized Precipitation Index (SPI) and Copula functions. In this study, we utilized monthly rainfall data spanning 35 years to estimate three critical characteristics of droughts: duration (D), severity (S), and Intensity (I). To determine the best fit marginal distribution of each univariate drought characteristic, we employed five commonly used probability distribution functions (PDFs). We conducted Kolmogorov-Smirnov (K–S) and Anderson-Darling (A-D) tests.

The bivariate modelling for the joint D-S, S–I, and I-D datasets involves fitting Archimedean families such as Frank, Clayton, Gumbel, and Joe copulas. To perform the trivariate modelling, two meta elliptical copulas, including Normal and Frank, and two Archimedean families, namely Clayton and Gumbel, are fitted using the test statistics BIC (Bayesian Information Criterion) and AIC (Akaike Information Criterion). The cross-validation process using Maximum Likelihood Estimation (MLE) is employed to identify the most appropriate Copula model based on its goodness of fit. This step is crucial for selecting the best model to accurately describe the joint behaviour of drought characteristics. Once the best-fit Copula model is determined, it is utilized to estimate the return period of various drought characteristics, thereby facilitating the investigation of their joint return period. Furthermore, the distribution of S, D, and I classes is categorized into different return periods (T) to facilitate drought management planning.

The findings revealed moderate drought conditions were recorded for SPI 1 and SPI 3 with a 2–5 years return period. For SPI 1, this drought class remains seasonal even for higher return periods. Further, the drought class transitions from seasonal to quarter for SPI 3 and a return period of 10–50 years. Regarding SPI 6 and SPI 12, the drought class is seasonal for a return period of 2 years, but it later progresses into the quarter to long-term drought class.

Tsunami is one of the deadliest natural disasters that mankind has ever experienced. Over the centuries, tsunami is known to have caused massive destruction owing to widespread loss and damage to property and human life. This report deals with the tsunami of 28th September 2018, which hit the Island nation of Indonesia, especially the Central Sulawesi Island. Indonesia's geographical location along the pacific ring of fire makes the nation exceptionally prone to strong tsunami. The tsunami under consideration in this study was triggered by a strong earthquake of magnitude (Mw) 7.5 scale. Usually, a strike-slip earthquake never leads to tsunami, but this tsunami was quite unexpected as the fault mechanism involved was strike-slip (strike slip along the Palu-Koro fault). The local geology, geography and tectonic configuration are crucial parameters in determining the tsunami hazard in an area. This report tries to examine the causative factors and mechanism behind the occurrence of tsunami. Secondary factors like funnelling and bay effect, submarine landslips, liquefaction and landslides which could have amplified the effects of the tsunami are also presented. The study provides a conclusive account of the related foreshocks and aftershocks associated to the event. A study of the losses incurred, causalities and other losses has also been attempted by comparing a timeline of satellite imageries. A statistical study was made from the datasets obtained from various catalogues from 1500 till date and the salient results are highlighted.