Granular flows are common on the Qinghai–Tibet Plateau and the Hengduan Mountains in China, and their dynamic process processes have obvious erosional and entrainment effects. On the one hand, the volume of the granular flow increases by a factor of several or ten, which significantly increases its ability to cause a catastrophe; on the other hand, the eroded loose material affects the granular flow dynamics process and changes its state of motion. In this paper, the dynamic mechanism of granular flow erosion and entrainment is investigated by DEM simulation. The effects of different substrate materials and substrate boundary conditions on granular flow erosion and entrainment are analyzed, and the effects of material mixing caused by erosion and entrainment on the state of motion of granular flow are discussed. It was verified that the kinetic mechanisms of granular flow erosion and entrainment includes impact erosion, ploughing, and shear abrasion. And discovered that small matrix particle size, small matrix boundary friction, and small matrix thickness lead to stronger ploughing and shear abrasion; Large matrix fractal dimensions result in stronger ploughing and weaker shear abrasion, and the granular flow does not entrain large amounts of material to the accumulation zone. Meanwhile, the dynamics of erosion and entrainment of granular flow were investigated, and the results showed that: 1. The greater the erosion rate, the greater the velocity and kinetic energy of the granular flow, the greater the distance traveled, and the smaller the apparent friction angle (i.e., the greater the mobility). 2. The amount of small granules in a granular flow changes its fluidity, the more small granules there are, the more fluid it is. 3. The fit reveals that the substrate fractal dimension has the strongest effect on the velocity and kinetic energy of granular flow, followed by substrate thickness and substrate boundary friction.
颗粒流在我国青藏高原和横断山脉十分常见，其动力过程过程具有明显的侵蚀和夹带效应。一方面，颗粒流的体积以几倍或十几倍的速度增大，致灾能力显著增强；另一方面，被侵蚀的松散物质会影响颗粒流的动力过程，改变其运动状态。本文通过 DEM 仿真研究了颗粒流侵蚀和夹带的动力学机理。分析了不同基质材料和基质边界条件对颗粒流侵蚀和夹带的影响，讨论了侵蚀和夹带引起的物质混合对颗粒流运动状态的影响。验证了颗粒流侵蚀和夹带的动力学机制包括冲击侵蚀、犁蚀和剪切磨蚀。并发现基质粒径小、基质边界摩擦力小、基质厚度小会导致较强的犁蚀和剪切磨损；基质分形尺寸大会导致较强的犁蚀和较弱的剪切磨损，颗粒流不会夹带大量物质到堆积区。同时，研究了颗粒流的侵蚀和夹带动力学，结果表明： 1：1.侵蚀速率越大，颗粒流的速度和动能越大，移动距离越远，表观摩擦角越小（即流动性越大）。2.颗粒流中小颗粒的数量会改变其流动性，小颗粒越多，流动性越强。3.拟合结果表明，基底分形维度对颗粒流动的速度和动能影响最大，其次是基底厚度和基底边界摩擦。
{"title":"Study on dynamic mechanism of granular flow erosion and entrainment based on DEM theory","authors":"Xurong He, Xiewen Hu, Zihao Huo, Jianfeng Tang, Shilin Zhang","doi":"10.1186/s40677-024-00278-5","DOIUrl":"https://doi.org/10.1186/s40677-024-00278-5","url":null,"abstract":"Granular flows are common on the Qinghai–Tibet Plateau and the Hengduan Mountains in China, and their dynamic process processes have obvious erosional and entrainment effects. On the one hand, the volume of the granular flow increases by a factor of several or ten, which significantly increases its ability to cause a catastrophe; on the other hand, the eroded loose material affects the granular flow dynamics process and changes its state of motion. In this paper, the dynamic mechanism of granular flow erosion and entrainment is investigated by DEM simulation. The effects of different substrate materials and substrate boundary conditions on granular flow erosion and entrainment are analyzed, and the effects of material mixing caused by erosion and entrainment on the state of motion of granular flow are discussed. It was verified that the kinetic mechanisms of granular flow erosion and entrainment includes impact erosion, ploughing, and shear abrasion. And discovered that small matrix particle size, small matrix boundary friction, and small matrix thickness lead to stronger ploughing and shear abrasion; Large matrix fractal dimensions result in stronger ploughing and weaker shear abrasion, and the granular flow does not entrain large amounts of material to the accumulation zone. Meanwhile, the dynamics of erosion and entrainment of granular flow were investigated, and the results showed that: 1. The greater the erosion rate, the greater the velocity and kinetic energy of the granular flow, the greater the distance traveled, and the smaller the apparent friction angle (i.e., the greater the mobility). 2. The amount of small granules in a granular flow changes its fluidity, the more small granules there are, the more fluid it is. 3. The fit reveals that the substrate fractal dimension has the strongest effect on the velocity and kinetic energy of granular flow, followed by substrate thickness and substrate boundary friction.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141512065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.1186/s40677-024-00276-7
Lee Ting Chai, Anand Nainar, Rodeano Roslee, Wilson Vun Chiong Wong, M. Phua
{"title":"Assessment of immediate and five-year earthquake impacts on river systems in sabah, Malaysia using multi-temporal satellite imageries","authors":"Lee Ting Chai, Anand Nainar, Rodeano Roslee, Wilson Vun Chiong Wong, M. Phua","doi":"10.1186/s40677-024-00276-7","DOIUrl":"https://doi.org/10.1186/s40677-024-00276-7","url":null,"abstract":"","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-07DOI: 10.1186/s40677-024-00277-6
Danang Sri Hadmoko, Sandy Budi Wibowo, Dimas Salomo J. Sianipar, Daryono Daryono, Mohammad Naufal Fathoni, Rohanita Setia Pratiwi, Eko Haryono, Franck Lavigne
On November 21, 2022, a magnitude Mw 5.6 earthquake struck Cianjur Regency in the West Java Province of Indonesia. It was followed by at least 512 aftershocks that persisted from November to June 2023. This seismic event occurred in an area previously unrecognized as an active fault zone. The consequences of this earthquake in Cianjur were severe, leading to both loss of life and extensive structural damage. The substantial damage to buildings was likely a result of abrupt alterations in the local topography due to surface deformation effects. This research endeavor aims to spatially determine the patterns of ground surface deformation and its relationship with local geomorphological setting due to earthquakes in Cianjur in 2022. In this study we conduct seismological analysis of 45 seismic stations, statistical analysis of mainshock and aftershocks data, RADAR Sentinel-1 imagery and employed the DInSAR methodology. Field survey was also conducted to determine the geomorphological characteristics in the study area. The outcomes disclosed that the deformation encompassed both subsidence and uplift. The results signify that there was subsidence deformation in the vicinity of Cianjur and its environs during the primary earthquake on November 21, 2022, with an average deformation value of approximately -5 cm. In contrast, the measured deformation during the aftershocks exhibited uplift deformation, with an average value of 10 cm. The examination of deformation patterns amid the 2022 Cianjur earthquake sequence detects elevated deformation values in the vicinity of Cugenang district, with an orientation running from northwest to southeast. The geomorphological investigation conducted indicates that the region of Cianjur encompasses a variety of landforms, such as volcanic, structural, fluvial, and denudational. These landforms exhibit distinct responses to seismic activities. Co-seismic hazards, such as landslides frequently occur as a consequence of seismic events in mountainous terrain. Spatio-temporal variation of ground deformation could arise from various causes, such as the number and distribution of aftershocks, stress redistribution, fault interactions, secondary effects, and local geological settings. The mainshocks release accumulated stress along a fault, resulting in particular types of deformation, whereas aftershocks may redistribute stress exhibiting on adjacent faults. Secondary effects triggered by aftershocks, coupled with local geological and geomorphological conditions, further contribute to the diverse patterns of ground deformation observed during seismic events. The results of the study revealed that ground deformation had the greatest impact on fluvial, volcanic, and denudational processes, resulting in notable subsidence and uplift in specific regions. The occurrence and magnitude of co-seismic landslides were triggered by both mainshock and aftershock events, which occurred on weathered geological materials. These effects wer
{"title":"Co-seismic deformation and related hazards associated with the 2022 Mw 5.6 Cianjur earthquake in West Java, Indonesia: insights from combined seismological analysis, DInSAR, and geomorphological investigations","authors":"Danang Sri Hadmoko, Sandy Budi Wibowo, Dimas Salomo J. Sianipar, Daryono Daryono, Mohammad Naufal Fathoni, Rohanita Setia Pratiwi, Eko Haryono, Franck Lavigne","doi":"10.1186/s40677-024-00277-6","DOIUrl":"https://doi.org/10.1186/s40677-024-00277-6","url":null,"abstract":"On November 21, 2022, a magnitude Mw 5.6 earthquake struck Cianjur Regency in the West Java Province of Indonesia. It was followed by at least 512 aftershocks that persisted from November to June 2023. This seismic event occurred in an area previously unrecognized as an active fault zone. The consequences of this earthquake in Cianjur were severe, leading to both loss of life and extensive structural damage. The substantial damage to buildings was likely a result of abrupt alterations in the local topography due to surface deformation effects. This research endeavor aims to spatially determine the patterns of ground surface deformation and its relationship with local geomorphological setting due to earthquakes in Cianjur in 2022. In this study we conduct seismological analysis of 45 seismic stations, statistical analysis of mainshock and aftershocks data, RADAR Sentinel-1 imagery and employed the DInSAR methodology. Field survey was also conducted to determine the geomorphological characteristics in the study area. The outcomes disclosed that the deformation encompassed both subsidence and uplift. The results signify that there was subsidence deformation in the vicinity of Cianjur and its environs during the primary earthquake on November 21, 2022, with an average deformation value of approximately -5 cm. In contrast, the measured deformation during the aftershocks exhibited uplift deformation, with an average value of 10 cm. The examination of deformation patterns amid the 2022 Cianjur earthquake sequence detects elevated deformation values in the vicinity of Cugenang district, with an orientation running from northwest to southeast. The geomorphological investigation conducted indicates that the region of Cianjur encompasses a variety of landforms, such as volcanic, structural, fluvial, and denudational. These landforms exhibit distinct responses to seismic activities. Co-seismic hazards, such as landslides frequently occur as a consequence of seismic events in mountainous terrain. Spatio-temporal variation of ground deformation could arise from various causes, such as the number and distribution of aftershocks, stress redistribution, fault interactions, secondary effects, and local geological settings. The mainshocks release accumulated stress along a fault, resulting in particular types of deformation, whereas aftershocks may redistribute stress exhibiting on adjacent faults. Secondary effects triggered by aftershocks, coupled with local geological and geomorphological conditions, further contribute to the diverse patterns of ground deformation observed during seismic events. The results of the study revealed that ground deformation had the greatest impact on fluvial, volcanic, and denudational processes, resulting in notable subsidence and uplift in specific regions. The occurrence and magnitude of co-seismic landslides were triggered by both mainshock and aftershock events, which occurred on weathered geological materials. These effects wer","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-26DOI: 10.1186/s40677-024-00275-8
A. Jaya Prakash, Sazeda Begam, Vít Vilímek, Sujoy Mudi, Pulakesh Das
Operational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms, such as Google Earth Engine and robust methodology (Otsu’s algorithm). The current study employs Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam State, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART), and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. Furthermore, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. The highest prediction accuracy was observed for the RF model (overall accuracy [OA] > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Over 26% of the study area indicated high flood hazard-prone areas, and approximately 60% showed high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as it helps to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness of emergency response efforts. Periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, and climate resilience studies.
{"title":"Development of an automated method for flood inundation monitoring, flood hazard, and soil erosion susceptibility assessment using machine learning and AHP–MCE techniques","authors":"A. Jaya Prakash, Sazeda Begam, Vít Vilímek, Sujoy Mudi, Pulakesh Das","doi":"10.1186/s40677-024-00275-8","DOIUrl":"https://doi.org/10.1186/s40677-024-00275-8","url":null,"abstract":"Operational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms, such as Google Earth Engine and robust methodology (Otsu’s algorithm). The current study employs Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam State, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART), and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. Furthermore, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. The highest prediction accuracy was observed for the RF model (overall accuracy [OA] > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Over 26% of the study area indicated high flood hazard-prone areas, and approximately 60% showed high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as it helps to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness of\u0000emergency response efforts. Periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, and climate resilience studies.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-04DOI: 10.1186/s40677-023-00262-5
Unashish Mondal, Anish Kumar, S. K. Panda, Devesh Sharma, Someshwar Das
The current research investigates into the application of various thunderstorm indices to predict severe thunderstorm occurrences during the monsoon season across four distinct regions in India. Methods: The study assesses the prediction model’s efficacy using various skill scores and the Weather Research and Forecasting (WRF) model has been integrated for 30 h with double moment microphysics scheme NSSL-17 which accurately reproduces vertical and meteorological measures. Furthermore, it investigates fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for forecasting severe thunderstorms. The results indicate that combining thunderstorm indices with skill scores, such as the Heidke Skill Score and True Skill Statistic, enhances the accuracy of severe thunderstorm predictions in the Indian monsoon season. The accurate predictions rely on determining optimal thresholds for each index. The study emphasizes the importance of using multiple indices rather relying solely on single measure for predicting severe thunderstorms. Advanced indices like the Energy Helicity Index (EHI) and Supercell Composite Parameter (SCP) perform well in forecasting extreme severe thunderstorms due to their strong reliance on wind shears. The EHI (> 1), and SCP (≥ 3.5), STP (≥ 1.2) along with low SRH at 3 km (100 m2/s2), indicated no evidence of helicity or tornado activity during the event. On the other hand, the CAPE, K Index, and VT Index demonstrate robust predictive capabilities for non-severe category thunderstorms. Integrating numerous thunderstorm indices improves meteorologists’ forecasts, ensuring public safety. Based on this work, future research can improve severe weather forecasting models’ accuracy and reliability.
{"title":"Comprehensive study of thunderstorm indices threshold favorable for thunderstorms during monsoon season using WRF–ARW model and ERA5 over India","authors":"Unashish Mondal, Anish Kumar, S. K. Panda, Devesh Sharma, Someshwar Das","doi":"10.1186/s40677-023-00262-5","DOIUrl":"https://doi.org/10.1186/s40677-023-00262-5","url":null,"abstract":"The current research investigates into the application of various thunderstorm indices to predict severe thunderstorm occurrences during the monsoon season across four distinct regions in India. Methods: The study assesses the prediction model’s efficacy using various skill scores and the Weather Research and Forecasting (WRF) model has been integrated for 30 h with double moment microphysics scheme NSSL-17 which accurately reproduces vertical and meteorological measures. Furthermore, it investigates fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for forecasting severe thunderstorms. The results indicate that combining thunderstorm indices with skill scores, such as the Heidke Skill Score and True Skill Statistic, enhances the accuracy of severe thunderstorm predictions in the Indian monsoon season. The accurate predictions rely on determining optimal thresholds for each index. The study emphasizes the importance of using multiple indices rather relying solely on single measure for predicting severe thunderstorms. Advanced indices like the Energy Helicity Index (EHI) and Supercell Composite Parameter (SCP) perform well in forecasting extreme severe thunderstorms\u0000due to their strong reliance on wind shears. The EHI (> 1), and SCP (≥ 3.5), STP (≥ 1.2) along with low SRH at 3 km (100 m2/s2), indicated no evidence of helicity or tornado activity during the event. On the other hand, the CAPE, K Index, and VT Index demonstrate robust predictive capabilities for non-severe category thunderstorms. Integrating numerous thunderstorm indices improves meteorologists’ forecasts, ensuring public safety.\u0000Based on this work, future research can improve severe weather forecasting models’ accuracy and reliability.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.1186/s40677-024-00274-9
M. G. Winter, T. Waaser, G. Fiddes
In August 2004 a series of debris flows caused significant disruption to the Scottish (strategic) Trunk Road Network. The subsequent Scottish Road Network Landslides Study identified a number of sites considered to be at highest risk. Some of these sites have been the subject of formal quantitative assessment of the risk from debris flow to mobile road users in vehicles. The A82 in Glen Coe has the added complication that two car parks have developed on debris fans exposing significant numbers of people to the risk while, they are essentially static and largely outside their vehicles. The risk to road users is determined using a previously developed probabilistic methodology for mobile road users (mobile elements at risk) and a new and related methodology developed for static road users (static elements at risk) is described and applied. Within the latter, an entirely new metric of Annual Average Daily Visits is used to allow the temporal component of the probability of a landslide impacting a person to be determined given the occurrence of an event. While Personal Individual Risk is at an acceptable level, including for frequent users, the risk presented to society as a whole presents a rather different picture; this is largely due to the number of visitors. The results assess the overall, societal risk for mobile elements at risk as As Low As reasonably Practicable, being at a similar level to other sites, albeit with a higher risk associated with higher numbers of fatalities. The results for the static elements at risk on the other hand suggest that the risks are classified as Unacceptable for higher numbers of fatalities. The assessment of the total societal risk, for mobile and static elements at risk, at the A82 Glen Coe suggests As Low As Reasonably Practicable for low numbers of fatalities but classify as Unacceptable for higher numbers of fatalities (around 20 to 250).
{"title":"Quantitative risk assessment for static and mobile road users: methodology and application at A82 Glen Coe, Scotland","authors":"M. G. Winter, T. Waaser, G. Fiddes","doi":"10.1186/s40677-024-00274-9","DOIUrl":"https://doi.org/10.1186/s40677-024-00274-9","url":null,"abstract":"In August 2004 a series of debris flows caused significant disruption to the Scottish (strategic) Trunk Road Network. The subsequent Scottish Road Network Landslides Study identified a number of sites considered to be at highest risk. Some of these sites have been the subject of formal quantitative assessment of the risk from debris flow to mobile road users in vehicles. The A82 in Glen Coe has the added complication that two car parks have developed on debris fans exposing significant numbers of people to the risk while, they are essentially static and largely outside their vehicles. The risk to road users is determined using a previously developed probabilistic methodology for mobile road users (mobile elements at risk) and a new and related methodology developed for static road users (static elements at risk) is described and applied. Within the latter, an entirely new metric of Annual Average Daily Visits is used to allow the temporal component of the probability of a landslide impacting a person to be determined given the occurrence of an event. While Personal Individual Risk is at an acceptable level, including for frequent users, the risk presented to society as a whole presents a rather different picture; this is largely due to the number of visitors. The results assess the overall, societal risk for mobile elements at risk as As Low As reasonably Practicable, being at a similar level to other sites, albeit with a higher risk associated with higher numbers of fatalities. The results for the static elements at risk on the other hand suggest that the risks are classified as Unacceptable for higher numbers of fatalities. The assessment of the total societal risk, for mobile and static elements at risk, at the A82 Glen Coe suggests As Low As Reasonably Practicable for low numbers of fatalities but classify as Unacceptable for higher numbers of fatalities (around 20 to 250).","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139969668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.1186/s40677-024-00272-x
Kongming Yan, Masakatsu Miyajima, Halil Kumsar, Ömer Aydan, Reşat Ulusay, Zhigang Tao, Ye Chen, Fawu Wang
On February 6, a successive rupture of major faults in the Eastern Anatolian Fault Zone and Cardak-Surgu fault triggered a strong mainshock (Mw 7.7) and a major aftershock (Mw 7.6) in Kahramanmaras. The successive earthquake sequence hit southern provinces in Türkiye and northern regions in Syria, causing severe fatality and economic loss. After the earthquakes, the International Consortium on Geo-disaster Reduction (ICGdR) organized an investigation team, involving specialists from China, Japan and Türkiye, to conduct a primary field reconnaissance on seismic damage of infrastructure and ground failures. The 10-day reconnaissance, including a mini-symposium at the Istanbul Technical University (ITU), was conducted from 25 March to 3 April and specifically focused on fault ruptures, liquefaction, landslide, rockfall and lateral spreading along the major ruptured faults from Antakya in Hatay to Goksun in Kahramanmaras, passing through provinces of Gaziantep, Adıyaman and Malatya. By this reconnaissance, a large amount of original seismic data was collected and a primary understanding was established for further steps on mitigation and reduction of seismic damages and its secondary geohazards.
{"title":"Preliminary report of field reconnaissance on the 6 February 2023 Kahramanmaras Earthquakes in Türkiye","authors":"Kongming Yan, Masakatsu Miyajima, Halil Kumsar, Ömer Aydan, Reşat Ulusay, Zhigang Tao, Ye Chen, Fawu Wang","doi":"10.1186/s40677-024-00272-x","DOIUrl":"https://doi.org/10.1186/s40677-024-00272-x","url":null,"abstract":"On February 6, a successive rupture of major faults in the Eastern Anatolian Fault Zone and Cardak-Surgu fault triggered a strong mainshock (Mw 7.7) and a major aftershock (Mw 7.6) in Kahramanmaras. The successive earthquake sequence hit southern provinces in Türkiye and northern regions in Syria, causing severe fatality and economic loss. After the earthquakes, the International Consortium on Geo-disaster Reduction (ICGdR) organized an investigation team, involving specialists from China, Japan and Türkiye, to conduct a primary field reconnaissance on seismic damage of infrastructure and ground failures. The 10-day reconnaissance, including a mini-symposium at the Istanbul Technical University (ITU), was conducted from 25 March to 3 April and specifically focused on fault ruptures, liquefaction, landslide, rockfall and lateral spreading along the major ruptured faults from Antakya in Hatay to Goksun in Kahramanmaras, passing through provinces of Gaziantep, Adıyaman and Malatya. By this reconnaissance, a large amount of original seismic data was collected and a primary understanding was established for further steps on mitigation and reduction of seismic damages and its secondary geohazards.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139921428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A huge number of sinkhole events has been recorded in different Italian urban areas, with an occurrence frequency largely increasing in the last decades, sometimes even causing loss of human lives. The main reason for such catastrophic events is the presence of man-made underground cavities, excavated within soft rocks, several decades ago and then abandoned, at shallow depths. Here, the possibility of interaction with overlying buildings and infrastructures and the corresponding sinkhole hazard is relatively high. In such contexts, the low mechanical properties of the soft rock formations where the cavities have been excavated, like those formed of calcarenites, which outcrop in large areas of Southern Italy, and their high susceptibility to weathering processes, represent one of the most important predisposing factors for instability. Therefore, assessing the stability of underground cavities is crucial for land management and planning purposes. The mechanically-based stability charts developed by Perrotti et al. (Int J Geomech 18(7):04018071, 2018) have proved to be a valid tool for preliminary stability assessment and, although allow to identify an eventual proneness of the cave to instability, they do not provide quantitative assessment about the safety margin itself. In that regard, this study intends to present the most recent outcomes obtained in the development of the methodology and is aimed at promoting an enhanced way for their application, so that the charts can become an operative tool for preliminary sinkhole hazard assessment in similar regions in the world.
{"title":"Assessing the stability of underground caves through iSUMM (innovative, straightforward, user-friendly, mechanically-based method)","authors":"Federica Angela Mevoli, Nunzio Luciano Fazio, Michele Perrotti, Piernicola Lollino","doi":"10.1186/s40677-023-00264-3","DOIUrl":"https://doi.org/10.1186/s40677-023-00264-3","url":null,"abstract":"A huge number of sinkhole events has been recorded in different Italian urban areas, with an occurrence frequency largely increasing in the last decades, sometimes even causing loss of human lives. The main reason for such catastrophic events is the presence of man-made underground cavities, excavated within soft rocks, several decades ago and then abandoned, at shallow depths. Here, the possibility of interaction with overlying buildings and infrastructures and the corresponding sinkhole hazard is relatively high. In such contexts, the low mechanical properties of the soft rock formations where the cavities have been excavated, like those formed of calcarenites, which outcrop in large areas of Southern Italy, and their high susceptibility to weathering processes, represent one of the most important predisposing factors for instability. Therefore, assessing the stability of underground cavities is crucial for land management and planning purposes. The mechanically-based stability charts developed by Perrotti et al. (Int J Geomech 18(7):04018071, 2018) have proved to be a valid tool for preliminary stability assessment and, although allow to identify an eventual proneness of the cave to instability, they do not provide quantitative assessment about the safety margin itself. In that regard, this study intends to present the most recent outcomes obtained in the development of the methodology and is aimed at promoting an enhanced way for their application, so that the charts can become an operative tool for preliminary sinkhole hazard assessment in similar regions in the world.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Earthquakes and landslides pose significant threats to human safety and property, necessitating early warning systems. However, the high construction costs of earthquake early warning systems present a challenge. Landslide warnings are more prevalent, so linking them to earthquake warnings could address cost concerns. Hence, it is crucial to validate the feasibility of utilizing GNSS landslide monitoring as assistance for earthquake early warning systems. This paper analyzes acceleration anomaly data from 31 GNSS landslide monitoring points near the epicenter of the May 2, 2023, MW = 5.2 Baoshan earthquake in Yunnan. The response time was determined as the time difference between an earthquake's occurrence and GNSS's acceleration anomalies. This calculation helps measure the time delay and sensitivity between these two events. Data were obtained from the geological disaster monitoring and early warning management system. GNSS landslide monitoring showed high sensitivity to nearby earthquakes. The fastest response time among the 31 data points was 8 seconds, while the slowest was 56 seconds, all falling within the one-minute mark. A linear correlation was found between acceleration anomaly response time and distance from the epicenter, indicating the feasibility of GNSS landslide monitoring-assisted earthquake monitoring. A proposal is made for a GNSS landslide monitoring cluster to establish a multi-dimensional landslideearthquake disaster warning system. This approach offers new methods for combining earthquake and landslide early warning systems, leveraging existing infrastructure for cost-effectiveness and enhancing disaster preparedness.
{"title":"Study on the correlation between real-time GNSS landslide acceleration monitoring and earthquake response: a case of May 2, 2023, MW = 5.2 Baoshan earthquake, Yunnan","authors":"Zhigang Tao, Mengnan Li, Qiru Sui, Yuting Mao, Manchao He, Yuebin Jiang","doi":"10.1186/s40677-024-00273-w","DOIUrl":"https://doi.org/10.1186/s40677-024-00273-w","url":null,"abstract":"Earthquakes and landslides pose significant threats to human safety and property, necessitating early warning systems. However, the high construction costs of earthquake early warning systems present a challenge. Landslide warnings are more prevalent, so linking them to earthquake warnings could address cost concerns. Hence, it is crucial to validate the feasibility of utilizing GNSS landslide monitoring as assistance for earthquake early warning systems. This paper analyzes acceleration anomaly data from 31 GNSS landslide monitoring points near the epicenter of the May 2, 2023, MW = 5.2 Baoshan earthquake in Yunnan. The response time was determined as the time difference between an earthquake's occurrence and GNSS's acceleration anomalies. This calculation helps measure the time delay and sensitivity between these two events. Data were obtained from the geological disaster monitoring and early warning management system. GNSS landslide monitoring showed high sensitivity to nearby earthquakes. The fastest response time among the 31 data points was 8 seconds, while the slowest was 56 seconds, all falling within the one-minute mark. A linear correlation was found between acceleration anomaly response time and distance from the epicenter, indicating the feasibility of GNSS landslide monitoring-assisted earthquake monitoring. A proposal is made for a GNSS landslide monitoring cluster to establish a multi-dimensional landslideearthquake disaster warning system. This approach offers new methods for combining earthquake and landslide early warning systems, leveraging existing infrastructure for cost-effectiveness and enhancing disaster preparedness.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ground failures in a slope due to gravity, are commonly known as landslides. Depend on the compositional, geological, and structural characteristics of the unstable initiation zone and the erosional composition of the propagation zone decide the complete particle size distribution of the moving mass and its gradation. This information is most important for the study of downslope movement. Only laboratory sieve analysis cannot fulfil this target because the natural debris contains a wide range of particle sizes, especially boulders. The combined method of scaled image analysis and laboratory sieve analysis or the combined method of line-grid analysis and laboratory sieve analysis was proposed to fulfil the requirement. To study the proposed combined methods, five different locations within the downslope propagation zone from the Aranayake landslide in Sri Lanka were surveyed and analyzed. In image analysis, the high-resolution scaled image of deposited debris was analyzed by computer-based image analysis for particle sizes. Small particles were addressed by the laboratory sieve analysis using the representative debris sample taken from the same location. If the boulder sizes within the debris are too big to address this method, then the Line-grid method was performed. The particles in every 0.5 m along a measured line of debris deposition were measured in this method. If the selected location contains small particles that cannot measured manually, the representative sample was used for the laboratory sieve analysis to fulfil this range. The results of three locations indicated a 40% distribution of < 10 mm and a 60% distribution of > 10 mm representing the general distribution of the debris. Two distributions deviated from the general distribution that was surveyed and analyzed from special locations of the “near boundary of flow path” and “slope change zone” of the landslide. The combined methodology yielded successful results of complete particle size distribution for the wide range of particle sizes in debris. The variation of the particle size distribution curves of debris along the downslope depositions is planned to be used for the study of downslope propagation, damage zone assessment studies, and predicting the representative composition of future failures.
{"title":"Identification of the complete particle size distribution of landslide debris by the combined method of scaled image analysis, line-grid analysis and laboratory sieve analysis","authors":"Sandaruwan Karunarathna, Satoshi Goto, Sajith Bandaranayake, Priyantha Bandara","doi":"10.1186/s40677-024-00270-z","DOIUrl":"https://doi.org/10.1186/s40677-024-00270-z","url":null,"abstract":"Ground failures in a slope due to gravity, are commonly known as landslides. Depend on the compositional, geological, and structural characteristics of the unstable initiation zone and the erosional composition of the propagation zone decide the complete particle size distribution of the moving mass and its gradation. This information is most important for the study of downslope movement. Only laboratory sieve analysis cannot fulfil this target because the natural debris contains a wide range of particle sizes, especially boulders. The combined method of scaled image analysis and laboratory sieve analysis or the combined method of line-grid analysis and laboratory sieve analysis was proposed to fulfil the requirement. To study the proposed combined methods, five different locations within the downslope propagation zone from the Aranayake landslide in Sri Lanka were surveyed and analyzed. In image analysis, the high-resolution scaled image of deposited debris was analyzed by computer-based image analysis for particle sizes. Small particles were addressed by the laboratory sieve analysis using the representative debris sample taken from the same location. If the boulder sizes within the debris are too big to address this method, then the Line-grid method was performed. The particles in every 0.5 m along a measured line of debris deposition were measured in this method. If the selected location contains small particles that cannot measured manually, the representative sample was used for the laboratory sieve analysis to fulfil this range. The results of three locations indicated a 40% distribution of < 10 mm and a 60% distribution of > 10 mm representing the general distribution of the debris. Two distributions deviated from the general distribution that was surveyed and analyzed from special locations of the “near boundary of flow path” and “slope change zone” of the landslide. The combined methodology yielded successful results of complete particle size distribution for the wide range of particle sizes in debris. The variation of the particle size distribution curves of debris along the downslope depositions is planned to be used for the study of downslope propagation, damage zone assessment studies, and predicting the representative composition of future failures.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139764705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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