Hasil untuk "Disasters and engineering"

Kate Baker, Sarah Ward, Lucinda Guy et al.

ABSTRACT Shifting away from traditional deficit‐based approaches like the ‘Decide‐Announce‐Defend’ model toward more inclusive and equitable community engagement presents both challenges and opportunities. Flood authorities often lack awareness or understanding of how to use effective active listening tools and methods to meaningfully engage and include flood‐affected communities. In this paper, we present two innovative methods rooted in the Asset‐Based Community Development framework, which emphasise listening and dialogue, and have been shown to build relationships and address power imbalances. Community Radio Practice (CRP) was used as a landing exercise to learn about the communities, and Walk in My Wellies (WiMW) was an initiative where Flood and Coastal Erosion Risk Management (FCRM) professionals were paired with community leaders. Talking, listening and communicating in the ‘right’ way was discussed at length in the WiMW programme, and the CRP approach led professional stakeholders such as local authorities to shift their preconceptions, and understand the importance of their relationships with the community. These innovative talking initiatives created ‘safe’ spaces to understand different perspectives; FCRM professionals and community members listened to each other with openness, honesty and respect, identifying barriers and sharing ideas to increase effective community engagement. CRP created lightbulb moments for FCRM professionals revealing the value of engaging with communities and power asymmetries began to shift, as did awareness of additional engagement approaches. Both CRP and WiMW highlighted the need for appropriate training, as enhancing FCRM professionals' understanding of community engagement is critical to reducing barriers and supporting a more equitable distribution of power and agency.

Zaoying Bi, Shanlei Sun, Qianrong Ma et al.

Abstract Choosing effective models for accurately estimating potential evapotranspiration (PET) is essential in various fields, including climatology, ecology, hydrology, and agronomy. However, many currently dominant PET products rely on multiple models with default parameters, which can introduce uncertainties into PET-related research. To address this issue, we derived the parameters for five widely used PET models using observations from 124 eddy covariance sites worldwide. By comparing their performances across various biomes, we identified the calibrated Priestley-Taylor and Milly-Dunne models as the optimal choices, capable of being applied effectively beyond their original observation sites. Using these optimal models, along with four widely-used meteorological datasets and annual land use and land cover data, we generated a monthly PET dataset at a 0.1° resolution during 1992–2022 across global vegetation zones. Finally, we compared the new dataset with the Global Land Evaporation Amsterdam Model V4.2a PET, including daily comparisons for each biome and annual trend comparisons during 1992–2022. This new PET dataset serves as an alternative resource for conducting PET-related research.

Guangdi Chen, Xiefei Zhi, Shuyan Ding et al.

Abstract Accurate high‐resolution temperature forecasting is of great significance for the economic and social development of humanity. Due to the chaotic nature of the atmosphere and the limitations of computational resources, model forecasts often lack sufficient resolution and exhibit systematic biases. Therefore, downscaling methods with smaller computational demands have become a good alternative. This study designed a super resolution generative adversarial network (SRGAN) for temperature downscaling, applying it to the 2 m temperature forecasts for the Southwest region of China from the Global Ensemble Forecasting System (GEFS), with forecast lead times of 1 to 7 days. Meanwhile, linear regression (LR), along with two advanced deep learning downscaling methods, U‐Net and super resolution deep residual networks (SRDRNs), were also used as benchmarks. The study shows that both deep learning methods, SRGAN and SRDRNs, can effectively address the issue of blurred temperature fields that may occur when using U‐Net. By comparing the Nash‐Sutcliffe Efficiency coefficient (NSE), pattern correlation coefficient (PCC), root mean square error (RMSE), and peak signal‐to‐noise ratio (PSNR), we found that SRGAN demonstrated the best performance among the four methods. In this work, a suitable loss function was set using the VGG network to help SRGAN better capture small‐scale details. Additionally, a mean square error decomposition method was used to further diagnose the sources of errors in different models, revealing their ability to calibrate various error sources. The results show that SRGAN, SRDRNs, and LR perform best in correcting the square of the bias (Bias2), while U‐Net is most effective in correcting the sequence errors.

Xiaoyuan Li, Shanlei Sun, Yongjian Ren et al.

As climate change exacerbates, droughts have become more intense, causing significant, widespread, and enduring adverse effects on carbon dynamics within terrestrial ecosystems. Therefore, it is crucial to quantitatively assess how drought influences carbon fluxes in these ecosystems. This understanding is essential for improving predictions of ecosystem responses to water stress and providing critical information to mitigate the effects of drought. In this study, we collected daily observations from 2004 to 2020 at two eddy covariance sites: one in an evergreen needle-leaved forest (abbreviated as ENFTha) and the other in grassland (abbreviated as GRAGri), both located in the North Temperate Zone. We first quantified the impacts of growing season (GS) droughts (GSD) on gross primary productivity (GPP) and ecosystem respiration (RECO). Our results indicated contrasting impacts of GSD on the two ecosystems: GPP and RECO increased in ENFTha, while they decreased in GRAGri. To explain these contrasting effects, we developed XGBoost models for GPP and RECO in ENFTha and GRAGri during the GS using eight environmental variables. We then applied the TreeExplainer-based SHapley Additive exPlanations framework to assess the significance of these variables in regulating GS carbon fluxes and to analyze their contributions to changes in GSD GPP and RECO. During the GS, four environmental variables—downwelling shortwave radiation (SW), vapor pressure deficit (VPD), longwave radiation (LW), and soil water content (near-surface air temperature (TA), SWC, SW, and LW)—significantly influenced GPP (RECO) in ENFTha and GRAGri, although their importance varied between the two ecosystems. These variables and their interactions also had nonlinear effects on GS GPP and RECO, with distinct threshold effects observed. In ENFTha, the enhanced SW demonstrated greater interaction effects with VPD, leading to increased GPP during GSD. In contrast, the rise in RECO could be attributed to the direct effects of TA. On the other hand, the declined SWC determined the decreases in carbon fluxes in GRAGri.

Yang Shi, Yilong Li, Zhenguo Zhang

Abstract Earthquakes in the Taiwan region have caused significant economic losses. We develop a model to assess seismic economic losses in the Taiwan region using the records of economic losses caused by historical earthquakes. Unlike existing models, we introduce Gross National Income Per Capita (GNIPC) as a parameter that responds to the influence of socio-economic development. The results show that our model can accurately estimate the earthquake economic losses in Taiwan region. The difference between the results of this model and those of existing models is evident, indicating differences between the Taiwan region and mainland China regarding geological background, seismic tectonics, and social resistance to earthquakes. The prediction results also imply that the society significantly underestimates the seismic economic losses in the Taiwan region. Our model can help the Taiwan region in disaster prevention and preparedness, contingency planning, allocation of relief resources, and post-disaster socio-economic recovery.

Muhammed Başpınar, Veli Yavuz

A thunderstorm (TS) is a severe weather phenomenon characterized by intense showers, hail, lightning, thunder, tornadoes, turbulence, and gusty winds. It occurs in vertically developed Cumulonimbus (Cb) clouds due to atmospheric instability and has a spatial resolution (meso-scale) of 1 - 100 km. This phenomenon occurs mainly in spring and early summer in our country due to the uplift and condensation of moist and unstable air masses. However, in recent years, these events have become more frequent during mid-summer, early autumn, and even winter months. In this study, the characteristics of TS events in Istanbul, covering the period from 2013 to 2022, including the warm season (May-September) and the cold season (October-April), were examined, and the atmospheric conditions under which they occur were identified. Aviation observations from Sabiha Gökçen International Airport were used to determine the occurrence of TS events, and data from Istanbul Kartal Radiosonde Station were analyzed to obtain upper-level atmospheric parameters. The occurrence of individual TS events or their association with other phenomena was investigated, and the statistics were presented. The daily, monthly, and yearly analyses of TS events during the period were conducted, and the changes in meteorological parameters for both the warm and cold seasons were statistically presented. Meteorological parameters such as temperature, dew point temperature, wind speed, wind direction, and relative humidity were analyzed for both warm and cold season TS events. Additionally, the average changes in each thermodynamic index and atmospheric stability parameter for hot and cold season oraj events were examined using a total of eight indices and parameters.

Subhadarsini Das, John T. Allen

Abstract Large hail causes significant economic losses in the United States each year. Despite these impacts, hail is not typically included in building and infrastructure design standards, and assessments of hazards from extreme hail size remain limited. Here, we use a novel approach and multiple hail size datasets to develop a new Generalized Extreme Value model through a Bayesian framework to identify large hail-prone regions across the country at 0.25° × 0.25°. This model is smoothed using Gaussian process regression for nationwide estimation of return likelihood. To contextualize local risk, hazard returns intersecting high-population exposure centers are compared. Fitted extreme value models suggest earlier work likely underestimates the hail hazard. Especially for higher return periods, the Bayesian approach is found to better model very rare hail occurrences than traditional approaches. This provides a framework for appreciating underlying risk from hail and motivates mitigative approaches through improving design standards.

Caoyuan Niu, Zhe-ming Zhu, Lei Zhou et al.

Lian-Sheng Gu, Xia-ting Feng, R. Kong et al.

C. Ng, Qi Zhang, Chao Zhou et al.

Nur Sinem Parti̇göç, Ceyhun Dinçer

Urban settlements currently face many disasters that are increasing in number and frequency every day such as floods, landslides, sea level rise, storms, drought, forest fires, etc. due to the negative consequences of global climate change caused by significantly exceeding the carrying capacity of nature. Cities are currently becoming less resistant to disaster risks because of the unhealthy settlements. These settlements are becoming widespread in terms of physical, economic and social conditions as a natural result of the urbanization in which planning processes are carried out in an uncontrolled and unsupervised manner. This situation makes it a necessity to develop strategies and policies aimed at reducing and/or eliminating possible disaster damages which are defined as high risk. From this viewpoint, the study aims to reveal the current disaster risks in urban areas with a dense population and building stock. Izmir city is determined as the study area. Spatial analyses are performed for disaster risk by using Geographical Information Systems (GIS) tools and Weighted Overlap method considering the natural and built environment elements that significantly affect the disaster risk. The findings obtained from the study and the determinations made within the scope of the Izmir Provincial Disaster Risk Reduction Plan (IRAP) prepared by the Disaster and Emergency Management Presidency (AFAD) are evaluated comparatively. As a result, it has been seen that the determinations put forward for disaster risks have shown consistency throughout Izmir city and the studies carried out at the local level have been prioritized in such a way as to be aimed at risk management and prevention planning by the time.

Abdul Majid Soomro, A. Naeem, Biswaranjan Senapati et al.

A disaster is an event that obstructs the regular course of events. Communication between nodes is severely hindered during disasters. As a result, an ad-hoc network method was necessary to manage global emergency circumstances. During disaster management, the role of information technology in terms of communication between nodes is critical. A mobile ad-hoc network is one that self-organizes and controls itself without the aid of centralized management in unpredictably changing circumstances. Mobile ad hoc network (MANET) technology can be highly helpful in emergencies. In a crisis, route discovery in MANET is the most important aspect of data transfer. Table-driven algorithms and link-state routing techniques, especially in a disaster, may not always deliver greater performance stressed out and in a lot of traffic. Low memory and power requirements make the hybrid technique ideal for quick convergence. Fast route discovery for network density is made possible by this study’s enhanced hybrid approach, which combines two on-demands and one table-driven routing protocol. The suggested approach was tested in a mock environment. Based on the outcomes of the simulation, when compared to the ad hoc on-demand distance vector (AODV) routing protocol, In terms of end-to-end delay, routing load and throughput, and data packet delivery density, it delivers a higher network density. The IHRP performs better and improves from 9% to 12%, according to results comparing it to the present regimen.

Chengyu Xie, Ziwei Chen, Guanpeng Xiong et al.

Rui Li, Changtai Zhou, Yizi Fu et al.

Longyong Shu, Zhengshuai Liu, Kai Wang et al.

Shucai Li, Cong Liu, Zong-qing Zhou et al.

Abstract Water inrush is one of the most serious geological disasters threatening tunnel construction. Generally, complexity and multi-sources feature of physical information existing in tunnel construction make disaster prediction very difficult, how to accurately predict the disaster becomes a hot topic in the field of tunnel engineering. Dempster-Shafer (DS) theory of evidence is a widely used method for reasoning with multiple evidences, however some unbelievable results usually appear in dealing with highly conflicting evidences by its traditional combination rule. Thus an improved fusion algorithm based on weighted average of evidence conflict probability was firstly introduced into risk prediction of water inrush disaster. Through the improved algorithm, multi-sources precursor information measured from previous model test were fused to predict quantitative risk levels of water inrush for different excavation step of subsea tunnel in the model test. The predicted high risk at the 12th excavation step by improved algorithm agreed well with actual phenomenon of intensive seepage observed in the test, while the traditional method gave a lower level. Moreover, the improved algorithm predicted a more accuracy result in the phase of water inrush (at 16th excavation step shown in test). In brief, the improved algorithm can make more accuracy prediction for water inrush disasters and will provide valuable reference for similar engineering.

Yong-Su Song, Hao Tan, Huimin Yang et al.

Abstract Long-term freeze-thaw (F-T) action causes the expansion of the fracture network in rock mass with ice-filled joints, triggering more serious engineering geological disasters. This study explores F-T damage effects and loading failure in double-fractured white sandstones with different saturation types (saturated and ice-filled). Saturated intact, saturated fractured, and ice-filled fractured sandstones were subjected to F-T cycle tests and uniaxial compression tests. Three-dimensional images of the specimens under various F-T cycles and stress states were obtained using computed tomography. The evolution in fracture morphology was evaluated visually, with pore network model parameters used for quantitative characterisation. Sandstone fractures evolved according to four main processes: frost-heave cracking, progressive expansion of damage, rock bridge penetration under load, and shear failure. The proportion of connected pores was used to estimate the influences of F-T cycling on pore-structure connectivity and fracture expansion in different specimen layers. During F-T cycling, pore characteristics vary according to size: micropores (equivalent radius  60% of pores and increase in number more than pores of other sizes. Mesopores (100–500 μm) gradually decrease in number but their volume fraction remains >70%. Macropores (500–1000 μm) have the greatest increase in volume fraction. Early F-T cycling induces the connection of pore throats, while later cycling accelerates their expansion. Hydrostatic pressure increases are the leading cause of damage in ice-filled fractured sandstone, which exhibits greater variations in porosity, heterogeneity coefficient, and pore-throat parameters under F-T cycling than saturated fractured sandstone. The results provide insight into fracture evolution and failure characteristics in fractured rock masses with different saturation types (saturated and ice-filled) in cold regions.

Shouqing Lu, Wang Chengfeng, Mingjie Li et al.

Abstract Deformed coal provides favorable conditions for the occurrence of gas outburst disasters. The existing gas diffusion model cannot accurately calculate the lost gas amount during the sampling period of deformed coal. In order to solve the above problems, a new mathematical time-dependent diffusivity model (NMTDM) and its simplified model (SNMTDM) of deformed coal have been established and validated. At the same time, the performance and engineering application of models were also discussed. The results show that the fitting accuracy of NMTDM and SNMTDM to gas diffusion data is greater than that of unipore diffusion model (UDM) and simplified unipore diffusion model (SUDM), respectively, which indicates that it is reasonable to regard the diffusion coefficient of deformed coal as a time-dependent variable. The diffusion coefficient of adsorbed gas, diffusion coefficient of free gas and total diffusion coefficient of gas decrease with the increase of time, and SNMTDM can realize the accurate calculation of lost gas amount. However, when the application of SNMTDM extends beyond its applicable range, the calculating results of the model may deviate seriously from the actual values. The gas diffusion models established in this paper can improve the calculation accuracy of lost gas amount and gas content of deformed coal.

Liang Wang, Zhuang Lu, Daphne Chen et al.

Abstract Coal and gas outburst is one of the most harmful disasters in coal mine production. With the increase of mining depth, all the coal seams in the deep-and-thick coal seam group will be upgraded to outburst coal seams, resulting in no suitable first mining layer can be selected as the protective layer. In this case, coal and gas outburst disasters are more serious and outburst prevention and control is more difficult. In order to alleviate the gas disaster and eliminate the risk of gas outburst, a safe strategy was proposed in this study. In this strategy, a suitable soft rock layer adjacent to deep-and-thick coal seams in the Luling coal mine of Huaibei coalfield was selected as the first protective mining layer after safety, economic and technical feasibility argument. Meanwhile, the stereoscopic gas extraction and utilization system safety engineering was established. The effect in the mining process practice of the soft rock protective layer was investigated and verified. Practice shows that this safe strategy can effectively reduce the outburst risk of strong outburst deep-and-thick coal seams and a large amount of pressure relief gas can be extracted and utilized. It can provide a new safety idea for efficient exploitation of coal and gas resources in deep-and-thick coal seams in China.

Shibing Huang, Zexin Lu, Zuyang Ye et al.

Abstract Freeze-thaw action has caused many engineering geology disasters in cold regions, such as frost cracking of tunnel and pavement, rockfall of rock slope, falling off of building materials. The freeze-thaw damage of porous rock is mainly induced by the frost heaving pressure due to pore water freezing. Although some elastic crystallization theories have been proposed to model the freezing process of pore water, the long-term frost heave characteristics under cyclic freeze-thaw are rarely studied. The ice crystallization and frost heave theory under freeze-thaw have been developed in the elastoplastic regime in this study. A novel elastoplastic model has been proposed to estimate the long-term frost heave and frost damage under freeze-thaw. This model has considered the influence of the pore size, yield stress of matrix and degree of water saturation on the development of the frost heaving pressure in pores. During freezing, a plastic region will produce inside the matrix around the pore. This plastic region gradually expands outwards with the increase of this pressure. During thawing, the elastic deformation will be recovered but a considerable residual plastic strain is accumulated. However, the growth rate of the residual plastic strain will decrease under freeze-thaw due to the production of new empty voids. Therefore, the freezing of pore water in porous rock is an elastoplastic problem. Besides, the water saturation and mechanical properties of the porous rock are very important to quantify the frost heave. This study provides a better understanding of the frost damage of the porous rock and a reference for the stability of rock engineering under freeze-thaw in cold regions.