Natural Hazards

Laz, Orpita U.; Rahman, Ataur; Ouarda, Taha B. M. J.

doi: 10.1007/s11069-023-06115-6pmid: N/A

Compound extreme natural events cause a significantly larger impact than individual extreme events. Therefore, the urgency of exploring the climatology of compound events is growing. This paper is aimed to identify the current hotspots of compound heatwaves and droughts (CHD) and trends in their occurrence in southeast Australia. In this context, 61 weather stations were selected from the study area, and analyses were carried out over the extended summer season of the time period 1971–2021. The hotspots of CHDs in southeast Australia were identified considering both the total count of CHD days and 90th percentile of CHDs during the study period. The study period was divided into two periods 1971–2000 and 2001–2021, to assess the change in hotspots spatially and temporally. Four different attributes of CHDs based on the number, duration, severity and amplitude of CHDs were also calculated, and Mann–Kendall (MK) test followed by Sen’s slope was adopted to detect the trends in all these four attributes of CHDs. Trends in CHD indices were also calculated for the two periods of 1971–2000 and 2001–2021. For calculating the CHD, excess heat factor (EHF) was used to identify the heatwaves. In the case of drought identification, SPEI and SPI drought indices were adopted with aggregation of 3 and 126 antecedent months, and three different threshold values were selected to consider three levels of dry conditions, e.g. 0, − 0.5 and − 1.0. It has been found that more CHDs occurred on the eastern side of NSW and Queensland states. Furthermore, the total count of CHD days increased notably during the last two decades. The trends in CHD indices were found to be significant in the recent period (2001–2021), and there was no trend in the earlier period (1971–2000). The findings of this study will help to plan heat and drought-related emergency management in the study area.

Eskandari, Saeedeh; Ravanbakhsh, Hooman; Ahangaran, Yazdanfar; Rezapour, Zolfaghar; Pourghasemi, Hamid Reza

doi: 10.1007/s11069-023-06133-4pmid: N/A

Mazandaran province in northern Iran is one of the fire-prone areas in the country in which a wide area of its natural resources have been destroyed by fire in recent years. This research aimed to detect the spatiotemporal relationships between climatic variables and fire regimes in Mazandaran province in recent decades. The fire variables (dependent variables) were the number and area of fires. The climatic variables (independent variables) were seasonal temperature mean, seasonal maximum temperature mean, seasonal absolute maximum temperature, seasonal precipitation mean, seasonal relative humidity mean, seasonal wind speed mean, and seasonal maximum wind speed mean for 26 years (1996–2021). Pearson's correlation coefficient and regression models were used to investigate the temporal relationship between fire and climatic variables during study period. Data mining models were used to detect the spatial relationship between fire ignition and climatic parameters and to produce the fire danger maps. The fire occurrence map was obtained from Mazandaran Natural Resources and Watershed Administration and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. The climatic maps were obtained by interpolation methods in GIS. The weight of climatic parameters in fire ignition was determined using MDG and MDA statistics from random forest (RF) algorithm. Then different data mining models (logistic regression, random forest, support vector machine, and SVM-RF ensemble model) and 70% of actual fires were used for modeling fire danger in R software. The area under the curve and 30% of actual fires were applied for accuracy assessment of the models. Results of temporal relationships indicated that there are significant relationships among the number of fires and seasonal absolute maximum temperature, seasonal precipitation mean, and seasonal relative humidity mean. On the other hand, a significant relationship was observed between the area of fires and seasonal temperature mean. The results of spatial relationship demonstrated that seasonal temperature mean, seasonal precipitation mean, and seasonal relative humidity mean had the greatest spatial importance in fire ignition. The results of accuracy assessment of fire danger models indicated that SVM-RF and RF models were the best models for fire danger mapping. Therefore, using the maps obtained from these models, it is possible to predict the climate-caused fires in natural ecosystems of Mazandaran province.

Santosa, Budi Heru; Martono, Dwi Nowo; Purwana, Rachmadhi; Koestoer, Raldi Hendro; Susanti, Wiwiek Dwi

doi: 10.1007/s11069-023-06120-9pmid: N/A

Flood resilience has emerged as an essential element in flood risk management, emphasizing the need to enhance urban stakeholders' perceptual and mitigative capabilities to minimize vulnerability and mitigate the impacts of floods. Given the significance of reducing vulnerability, it becomes imperative to understand the full scope of resilience in flood risk management strategies. Therefore, the study proposed a framework for understanding flood resilience in an urban flood-prone area using a subjective approach in a study area in three flood-affected Subdistricts in Periuk District, Tangerang City, Indonesia. A mixed-method strategy was employed, combining quantitative data from 354 affected households with qualitative insights from in-depth interviews with ten neighborhood leaders. The quantitative approach utilized composite indicators, criteria weighting, and indices to evaluate flood resilience. The household questionnaire covered various factors influencing flood resilience, including social, economic, home environment, communication and information, social capital, institutional, and risk perception. The main finding of this study is that employing a subjective mixed method, incorporating quantitative and qualitative methodologies, enables a thorough assessment of household flood resilience. The results reveal that communication and information, social capital, institutional factors, and community perception exhibit notably very high indices, while criteria related to social, economic, and home environment factors attain relatively high scores. This study enhances the understanding of household flood resilience by employing a subjective approach, combining quantitative data from flood-affected households and qualitative data from neighborhood leaders. This framework expedites comprehension and yields reliable results.

Donovan, Amy; Pfeffer, Melissa; Barnie, Talfan; Sawyer, Georgina; Roberts, Tjarda; Bergsson, Baldur; Ilyinskaya, Evgenia; Peters, Nial; Buisman, Iris; Snorrason, Arní; Tsanev, Vitchko; Oppenheimer, Clive

doi: 10.1007/s11069-023-06114-7pmid: 37719282

The eruptions of Eyjafjallajökull volcano in 2010 (including its initial effusive phase at Fimmvörðuháls and its later explosive phase from the central volcano) and Bárðarbunga volcano in 2014–2015 (at Holuhraun) were widely reported. Here, we report on complementary, interdisciplinary observations made of the eruptive gases and lavas that shed light on the processes and atmospheric impacts of the eruptions, and afford an intercomparison of contrasting eruptive styles and hazards. We find that (i) consistent with other authors, there are substantial differences in the gas composition between the eruptions; namely that the deeper stored Eyjafjallajökull magmas led to greater enrichment in Cl relative to S; (ii) lava field SO2 degassing was measured to be 5–20% of the total emissions during Holuhraun, and the lava emissions were enriched in Cl at both fissure eruptions—particularly Fimmvörðuháls; and (iii) BrO is produced in Icelandic plumes in spite of the low UV levels.

Islam, Abu Reza Md. Towfiqul; Bappi, Md. Mijanur Rahman; Alqadhi, Saeed; Bindajam, Ahmed Ali; Mallick, Javed; Talukdar, Swapan

doi: 10.1007/s11069-023-06106-7pmid: N/A

Flood, a dangerous hydro-geomorphic hazard, is one of the most critically applied science research issue. The restoration and recovery are costly and can interrupt communities’ sustainable growth after the extensive flood. Flash floods (FF) are a frequent natural disaster that causes significant casualties and disrupts economic growth in the Brahmaputra River Basin (BRB). Hence, the flood susceptibility modeling of BRB is imperative. The study uses six machine learning (ML) techniques (three stand-alone such as artificial neural network (ANN), fuzzy logic (FL), and random forest (RF), and three hybrid ensemble models (HEMs) including ANN-FL, FL-RF, and RF-ANN) to appraise flash flood Susceptibility (FFS) prediction in BRB considering 16 flash flood susceptibility factors. Area under the curve (AUC), ROC curve, confusion matrix (CM), and Friedman test are applied to assess the performance of the models. Results for the training and testing datasets showed that all HEMs models for FFS prediction in the BRB outperformed the stand-alone models. The RF-ANN has the best prediction ability of all models because the RF meta-classifier improves the ANN model’s base-classifier precision. The RF-ANN model delineated 2908.46 km2 and 874.73 km2 areas as very high and high flood susceptible zones, whereas 995.99 km2, 702.48 km2, and 10,127.57 km2 areas were predicted as moderate, low, and very low flood susceptible zones. Slope, water, vegetation, PrC, aspect, and rainfall all make the BRB sensitive to FF, as per the analysis of InGR and PCM. This work’s accuracy of the ML HEMs used for FFS mapping is promising. Furthermore, the findings of this study may be valuable for flood prevention and management to deal with the current uncertainties and more precisely identify numerous characteristics that impact FFS. This research is helpful for policymakers because it provides information that could be utilized to develop measures to lessen the adverse effects of FF.

Chowdhury, Swaraj; Patra, Nihar Ranjan

doi: 10.1007/s11069-023-06136-1pmid: N/A

In the present study, pond ash from Panki thermal power plant, India (seismic zone III), has been reinforced with geogrid layers and the influence of reinforcement on dynamic shear modulus, material damping ratio, degradation index and resistance to liquefaction of pond ash samples has been investigated. The static and dynamic properties of ash samples without and with geogrid reinforcement have been determined by laboratory experiments. Further, these properties have been used in the dynamic response analysis of the two-dimensional domain of the Panki pond ash deposit that is pond ash column reinforced without and with geogrid. The OpenSees (Open System for earthquake engineering simulation) software is used to perform the analysis. Three moderate magnitude earthquakes (Chamba, Chamoli and Uttarkashi) of Himalayan origin have been considered to study the variations of acceleration, displacement and excess pore water pressure ratio with time for different layers of pond ash columns without and with geogrid reinforcement. Cyclic triaxial experiments show that due to the provision of geogrid reinforcement, the dynamic shear modulus increases about 13% to 81.6% and the liquefaction resistance increases about 91–162%. The dynamic response analysis shows that for geogrid-reinforced pond ash column, the peak ground acceleration (PGA) value decreases about 32–33%, 17–22% and 13.5–18% and the peak ground displacement (PGD) value decreases about 23.5–39%, 18.5–20% and 13–17% as compared to unreinforced pond ash column for Chamba, Chamoli and Uttarkashi earthquakes, respectively.

Guo, Yukun; Zhu, Jun; You, Jigang; Pirasteh, Saied; Li, Weilian; Wu, Jianlin; Lai, Jianbo; Dang, Pei

doi: 10.1007/s11069-023-06128-1pmid: N/A

Enhancing the capacity and awareness of individuals in disaster prevention and mitigation requires an intuitive and comprehensible method for representing flood hazard education knowledge. To address the challenges of complex information transfer and limited knowledge expression in flood disaster education, this paper proposes a novel strategy. The approach utilizes conceptual graphs to organize and guide the visual representation of flood disaster knowledge. It involves connecting flood data and knowledge elements using concept nodes and relationships, and translating them into dynamic visual representations through instantiation methods. A prototype system was developed to visualize disaster data obtained from flood-affected areas. The visualization output was compared to expert-based reports using a questionnaire, focusing on attractiveness and comprehensibility. The results demonstrated the superiority of our approach, with higher scores of 0.433 and 0.22 (on a scale of 0–1) for attractiveness and comprehensibility, respectively. This highlights the effectiveness of our approach in displaying flood knowledge and facilitating its dissemination. In summary, this paper introduces a comprehensive and dynamic visualization approach for the entire flood process, integrating relevant disaster knowledge. It presents a fresh perspective on digital disaster education tailored to floods, aiming to enhance public awareness of flood risk prevention.

Vaughn, Cole; Sherman-Morris, Kathleen; Poe, Philip

doi: 10.1007/s11069-023-06140-5pmid: N/A

How individuals trust or relate to local broadcast news personalities can help explain how they respond to hazardous weather events. Local media often share weather information on twitter, but research on the way audiences interact or engage with local media via twitter during hurricanes is limited. No research has compared engagement with local weathercasts on Twitter both before and during a hurricane, although engagement before an event may have an influence on engagement during an event. This article examines the evolution and interrelation of the content, timing, and engagement of tweets from local news media Twitter accounts downloaded from the Social Media Tracking and Analysis System at Mississippi State University during the approach, landfall, and dissipation of Hurricane Irma. Retweets were used as the engagement metric in this study. Tweets related to the hurricane showed an overall higher retweet rate, though this result was not universal. The tweets with the greatest retweet rate occurred once the studied location was within the cone of uncertainty but before impacts from Irma arrived. The news accounts run by news stations in larger markets were the main drivers of retweet engagement and tweet frequency fluctuation. The personal Twitter accounts of broadcast meteorologists at local news stations were analyzed for tweets about their personal life a few months prior to Irma’s impact. The accounts with relatively small follower counts showed a weak positive correlation between posting personal content and increased retweet rates in Irma. Those with larger follower counts showed a weak negative correlation between the variables. Results may help local news media make decisions regarding which accounts are likely to reach the greatest number of individuals during a hurricane.

Ozkula, Gulen; Dowell, Robert K.; Baser, Tugce; Lin, Jui-Liang; Numanoglu, Ozgun A.; Ilhan, Okan; Olgun, C. Guney; Huang, Cheng-Wei; Uludag, Tunc Deniz

doi: 10.1007/s11069-023-06143-2pmid: N/A

On February 6, 2023, a sequence of earthquakes hit Kahramanmaras, Turkey, with magnitudes of Mw = 7.8 and 7.5, at 4:17 am and 1:24 pm local time, respectively. According to the records, the Mw = 7.8 event was the biggest earthquake since the 1939 Erzincan earthquake of the same magnitude and second-strongest recorded after the 1668 North Anatolia Earthquake. However, it was the most devastating earthquake in the history of Turkey in terms of structural and geotechnical damage and fatalities caused by this. The objective of this article is to explore the aftermath of this major seismic event, with a particular focus on the following areas: (1) regional geology and seismotectonics background, along with geological field observations; (2) seismological context and analysis of strong ground motion records; (3) a summary of field reconnaissance findings; (4) an evaluation of residential structures, bridges, schools, hospitals, and places of worship, as well as, building foundations; (5) a study of soil and rock slopes, seismic soil liquefaction manifestations, rockfalls, earth dams, harbors, lifelines, ports, deep excavations, and retaining structures. The conclusions drawn herein are from the field reconnaissance and, therefore, are preliminary in nature. Subsequent research utilizing the gathered data will offer more comprehensive insights and definitive conclusions regarding the observations discussed.

Tang, Xiaoyan; Feng, Yongjiu; Gao, Chen; Lei, Zhenkun; Chen, Shurui; Wang, Rong; Jin, Yanmin; Tong, Xiaohua

doi: 10.1007/s11069-023-06131-6pmid: N/A

Drought is one of the most severe natural hazards influenced by many factors, which can in turn cause severe damage to agricultural, economic, social and ecological systems. For assessing drought intensity, early studies have typically used single-factor-based modeling approaches to delineate a specific aspect of drought. In this study, we developed an entropy weight method (named LNPS-EWM) for drought assessment based on MODIS products and Sentinel-1A images, considering four important factors, including land surface temperature (LST), normalized difference vegetation index (NDVI), potential evapotranspiration (PET), and soil moisture. The new LNPS-EWM method was applied to analyze the spatiotemporal drought patterns in Urumqi for 2018–2021. The results show that LST and PET were the dominant factors, which accounted for about 70% while NDVI and soil moisture only accounted for about 30%. A five-level drought classification shows that severe drought accounts for the largest portion and exceptional drought for the smallest portion. From 2018 to 2021, the Urumqi city center is the most drought-prone area, followed by the low-lying areas, while the southwestern and eastern mountainous areas are in a mild drought. In the central region in the north–south direction, the drought intensity in Urumqi was mitigated from 2018 to 2021. These results are useful for risk assessment, large-scale monitoring, and early warning of drought conditions. This study improves our understanding of drought intensity patterns in arid Northwest China and should help improve regulatory and regional policies to combat drought to maintain eco-friendly cities in other arid regions.