Ice Disaster Research Articles

Application of Unmanned Aerial Vehicle Remote Sensing System in Low Temperature Rain, Snow and Ice Disaster Monitoring

The unmanned aerial vehicle (UAV) remote sensing system, as a versatile and efficient monitoring tool, exhibits unique advantages in monitoring low-temperature rain, snow, and freezing disasters. Equipped with a variety of sensors, it is capable of collecting high-precision data even under extreme climatic conditions, thus addressing the deficiencies of traditional monitoring methods in terms of timeliness, coverage, and accuracy. Particularly when confronted with large-scale and rapidly evolving disasters such as snowstorms and ice freezes, UAVs can swiftly respond and transmit real-time data, providing disaster response departments with timely and reliable decision-making support. The application of UAV remote sensing systems in disaster data collection, processing, analysis, as well as real-time monitoring and early warning systems, renders it a central tool in contemporary disaster monitoring technology. Looking ahead, with the further advancement of remote sensing technologies and the integration of artificial intelligence and big data analytics, the role of UAVs in disaster monitoring will become even more pronounced. This paper will explore the specific applications of UAV remote sensing systems in low-temperature rain, snow, and freezing disasters, analyze the associated technical and managerial challenges and solutions, and forecast future development directions.

Modelling icing growth on overhead transmission lines: Current advances and future directions

AbstractThe increasing impact of climate change raises concerns regarding the vulnerability of overhead transmission lines to ice disasters. To address this issue, this study reviews icing growth modelling in two categories: physical‐driven models (PDMs) and data‐driven models (DDMs), covering current advances and future directions. First, PDMs are summarised, focusing on the thermodynamic and fluid mechanics mechanisms. Existing PDMs are compared based on principles, analysing their advantages, disadvantages, and challenges faced. Second, the summarisation of DDMs involves four aspects: data preparation, algorithm selection, model training, and model evaluation. In data preparation, techniques such as preprocessing methods are reviewed to handle multisource data. In algorithm selection, various modelling algorithms are compared and analysed, from basic to deep learning approaches. In model training, processes are summarised to enhance practical applicability, including data partitioning, hyperparameter adjustment, generalisation capability, and model interpretability. In model evaluation, the predictive capabilities are analysed, covering both regression and classification tasks. Subsequently, based on the analyses, a comparison of PDMs and DDMs across various aspects is presented. Finally, future directions in icing growth modelling are outlined. The aim is to enhance icing assessment by understanding the underlying mechanism in attempt to reduce vulnerability and ensure reliability against adverse weather conditions.

Threshold Ranges of Multiphase Components from Natural Ice CT Images Based on Watershed Algorithm

The multiphase components of natural ice contain gas, ice, unfrozen water, sediment and brine. X-ray computed tomography (CT) analysis of ice multiphase components has the advantage of high precision, non-destructiveness and visualization; however, it is limited by the segmentation thresholds. Due to the proximity of the CT value ranges of gas, ice, unfrozen water, sediment and brine within the samples, there is uncertainty in the artificial determination of the CT image segmentation thresholds, as well as unsuitability of the global threshold segmentation methods. In order to improve the accuracy of multi-threshold segmentation in CT images, a CT system was used to scan the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice. The threshold ranges of multiphase components within the ice were determined by watershed algorithm to construct a high-precision three-dimensional ice model. The results indicated that CT combined with watershed algorithm was an efficient and non-destructive method for obtaining microscopic information within ice, which accurately segmented the ice into multiphase components such as gas, ice, unfrozen water, sediment, and brine. The gas CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from −1024 Hu~−107 Hu, −1024 Hu~−103 Hu, and −1024 Hu~−160 Hu, respectively. The ice CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from −103 Hu~−50 Hu, −100 Hu~−38 Hu, −153 Hu~−51 Hu. The unfrozen water CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from −8 Hu~18 Hu, −8 Hu~13 Hu. The sediment CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from 20 Hu~3071 Hu, 20 Hu~3071 Hu, and the brine CT values of the Arctic sea ice ranged from −6 Hu~3071 Hu. The errors between the three-dimensional ice model divided by threshold ranges and measured sediment content were less than 0.003 g/cm3, which verified the high accuracy of the established microscopic model. It provided a scientific basis for ice engineering, ice remote sensing, and ice disaster prevention.

Overview: Results of Snow and Ice Disaster Mitigation Conducted by the National Research Institute for Earth Science and Disaster Resilience

More than half of Japan’s land area experiences significant snowfall during winter, and the damage caused by various snow and ice disasters remains a dire issue, which also leads to decreased living standards. Simultaneously, the nature of snow and ice disasters has been transformed due to climate change and the increasing occurrence of extreme weather conditions. The National Research Institute for Earth Science and Disaster Resilience (NIED) has been continuously conducting research to address these problems in relation to snow and ice disasters. This study presents the results of the project “Research on Combining Risk Monitoring and Forecasting Technologies for Mitigation of Increasingly Diverse Snow Disaster” conducted by the NIED over a seven-year period from April 2016 to March 2023. This project developed technology for conducting accurate observations of snowfall and snow cover conditions over wide areas as well as technology for areal prediction of snow and ice disasters through simulations. Based on collaboration with stakeholders, such as local governments, our study investigated how to optimize the use of our information products for snow and ice disaster mitigation. Through these insights, the NIED provides information for prompt and appropriate responses to snow and ice disasters, thus supporting safe and comfortable living in both snowy and non-snowy areas.

Special Issue on NIED Frontier Research on Science and Technology for Disaster Risk Reduction and Resilience 2024

We are pleased to publish this special issue, dedicated to the NIED Frontier Research on Science and Technology for Disaster Risk Reduction and Resilience 2024. The fifth NIED mid-/long-term plan period began in 2023, and this issue includes the first results of six papers, two reviews, one survey report, one note, and one material. Recently, violent natural disasters have triggered the development of effective countermeasures. Akita, Yamaguchi et al., and Hirashima et al. dealt with climate hazards such as heavy rainfall, snow, and ice disasters. Yamazaki-Honda reported the progress and challenges of disaster reduction and climate change adaptation using DRR strategies. Yadab et al., Akita, and Naito et al. reported data analysis and suggestions to evaluate seismic and volcanic hazards. Nagata et al. proposed the ICT tool “YOU@RISK” to educate regarding volcanic disaster risks. Sato et al. emphasized the importance and effectiveness of information technology. Kimura et al. emphasized preparedness and elaborated on measures to improve preparedness for all natural hazards. These new outcomes will promote the development of a sustainable society against these hazards and will be flagships to proceed with NIED research projects.

Typical Marine Ecological Disasters in China Attributed to Marine Organisms and Their Significant Insights.

Owing to global climate change or the ever-more frequent human activities in the offshore areas, it is highly probable that an imbalance in the offshore ecosystem has been induced. However, the importance of maintaining and protecting marine ecosystems' balance cannot be overstated. In recent years, various marine disasters have occurred frequently, such as harmful algal blooms (green tides and red tides), storm surge disasters, wave disasters, sea ice disasters, and tsunami disasters. Additionally, overpopulation of certain marine organisms (particularly marine faunas) has led to marine disasters, threatening both marine ecosystems and human safety. The marine ecological disaster monitoring system in China primarily focuses on monitoring and controlling the outbreak of green tides (mainly caused by outbreaks of some Ulva species) and red tides (mainly caused by outbreaks of some diatom and dinoflagellate species). Currently, there are outbreaks of Cnidaria (Hydrozoa and Scyphozoa organisms; outbreak species are frequently referred to as jellyfish), Annelida (Urechis unicinctus Drasche, 1880), Mollusca (Philine kinglipini S. Tchang, 1934), Arthropoda (Acetes chinensis Hansen, 1919), and Echinodermata (Asteroidea organisms, Ophiuroidea organisms, and Acaudina molpadioides Semper, 1867) in China. They not only cause significant damage to marine fisheries, tourism, coastal industries, and ship navigation but also have profound impacts on marine ecosystems, especially near nuclear power plants, sea bathing beaches, and infrastructures, posing threats to human lives. Therefore, this review provides a detailed introduction to the marine organisms (especially marine fauna species) causing marine biological disasters in China, the current outbreak situations, and the biological backgrounds of these outbreaks. This review also provides an analysis of the causes of these outbreaks. Furthermore, it presents future prospects for marine biological disasters, proposing corresponding measures and advocating for enhanced resource utilization and fundamental research. It is recommended that future efforts focus on improving the monitoring of marine biological disasters and integrating them into the marine ecological disaster monitoring system. The aim of this review is to offer reference information and constructive suggestions for enhancing future monitoring, early warning systems, and prevention efforts related to marine ecological disasters in support of the healthy development and stable operation of marine ecosystems.

Numerical simulation of hydraulic characteristics and pollutant transport in the channels with different ice coverages

ABSTRACT In winter, rivers at high latitudes often freeze and an ice cover emerges at the free surface of the river. According to the coverage degree of the ice cover, rivers can be divided into free-flowing, partially frozen, and completely frozen. The presence of ice cover can greatly alter the hydraulic conditions of rivers, complicating the transport of pollutants in river channels. The distribution of pollutants in rivers with different ice coverages was simulated using the Detached Eddy Simulation (DES) model in this paper. The velocity distribution, the pollutant diffusion coefficient distribution, the turbulent kinetic energy distribution, and the vortex distribution with different ice coverages were analyzed. The velocity in the ice cover zone is at a lower rate than in the open flow zone. With the increase of the ice coverage, the turbulent kinetic energy of the ice cover area in partially frozen conditions gradually increases, which makes the water flow turbulence increase. The diffusion coefficient of pollutants in the ice cover area is greater than that in the open flow area, so pollutants disperse more quickly in the ice cover area. The turbulence energy and the diffusion coefficient have similar distribution characteristics beneath the ice cover. The presence of the ice cover promotes the movement of the vortex from the open flow area to the ice cover area, which may be the reason for the larger turbulence energy and the diffusion coefficient. The simulation results can provide a reference for ice disasters and water quality remediation.

Recovery strategy of distribution network based on dynamic island rescue under extreme weather

AbstractThe frequent occurrence of various extreme weather has a great influence on the normal and stable operation of the distribution network. In order to minimise the large‐scale power loss of the distribution network caused by extreme weather, considering that most of the practical disasters are concurrent with various weather types, and each disaster is independent of each other. A unified failure rate calculation model is proposed, which includes line break, short circuit, tower fall, insulator flashover etc., to realise disaster scenario prediction. Secondly, a resilience evaluation index of island rescue based on mobile energy storage system (IR‐MESS) is proposed. Thirdly, considering the flexibility of MESS, the pre‐disaster scheduling of MESS is carried out according to the predicted disaster scenario. After the disaster occurs, a spatio‐temporal optimisation scheduling model based on the rescue state and charge state of MESS is proposed, and a dynamic IR‐MESS is formed to provide power supply for important loads in out‐of‐load areas of the distribution network. Finally, taking the actual ice disaster in northeast China as an example, the results show that the rescue strategy based on IR‐MESS proposed in this paper can effectively elevate the resilience of the distribution network.

Deep learning model optimization of 110 kV line ice-melting technology without power failure

Abstract In order to achieve ice disaster warning for transmission lines, this article proposes an improved method based on particle swarm optimization (PSO) backpropagation (BP) neural network for ice thickness prediction. The study selects multiple factors such as temperature, humidity, and wind speed, introducing neural network prediction methods to explore the situation of ice thickness on transmission lines. The predictions from this model demonstrate closer alignment with actual values and achieve superior prediction accuracy compared to both the BP model and the PSO-BP model, and the average absolute percentage error of improved PSO (IPSO)-BP is 0.007. Using a 110 kV line as the experimental object, the study conducts ice melting without power outage by changing of the grid currents. Additionally, this predictive model method is employed for ice warnings, assessing ice coverage levels by computing the ice coverage ratio. This facilitates precise control over the activation and deactivation of ice melting devices. The method proposed in this study addresses the issue of low accuracy resulting from the singular data types used in traditional early warning models. Future efforts should focus on further validating the applicability of this method under varying climatic and environmental conditions to achieve real-time, precise control over line ice melting.

Transmission Line Ice Disaster Early Warning Method Based on Ice Thickness Prediction Using GM(1, 6) Model

The operation of the electricity system faces the difficulty of minimizing ice damage to transmission lines. Due to the uncertainty of transmission line icing thickness, accurate prediction of icing thickness is very important to guide transmission line planning and power grid anti-icing design effectively. Scientific and reasonable early warning assessment of conductor icing is also helpful to make timely and accurate response measures to the possible freezing disaster risk, so as to effectively ensure the safe and stable operation of power grid and reduce the occurrence of the freezing disaster. Based on this, this paper proposes a multi-factor prediction model based on GM (1, 6) grey theory, which considers the transmission line conductor icing model under the influence of multiple meteorological factors. Based on the conductor icing model, the conductor icing degree can be predicted in real time according to meteorological parameters, so as to realize the purpose of transmission line conductor icing disaster risk early warning. It is worth noting that the paper improves the traditional grey model by adding random data functions, which makes the model solve the problem of inaccurate prediction of small samples. Finally, a case study is carried out, and the icing disaster risk is divided into five levels. It is found that the maximal prediction error of icing thickness based on GM (1, 6) grey theory multi-factor prediction model is 10.06%(The average value is only 4.22%), and the accuracy of transmission line conductor icing disaster risk early warning is 88.9%. In addition, a certain safety margin value is added to the predicted value near the critical value of icing thickness, reducing the probability of judging the high-risk level as low-risk. Applying risk early warning method in ice areas can guide the anti-ice work of transmission line.

Exploring knowledge gaps on atmospheric ice accretion

We spoke to Professor Muhammad Shakeel Virk about the work of the nICE project in investigating how atmospheric ice accumulates on ground structures. Researchers are also exploring new technological solutions to detect and mitigate ice on structures and streamlining strategies regarding ice disaster management for safe and costeffective human industrial operations in the High North and the Arctic.

Mobile Energy Storage System Scheduling Strategy for Improving the Resilience of Distribution Networks under Ice Disasters

The distribution system is easily affected by extreme weather, leading to an increase in the probability of critical equipment failures and economic losses. Actively scheduling various resources to provide emergency power support can effectively reduce power outage losses caused by extreme weather. This paper proposes a mobile energy storage system (MESS) scheduling strategy for improving the resilience of distribution networks under ice disasters. First, the influence of wind and ice loads on power transmission lines is analyzed, and a detailed fault statistical model of transmission lines under an ice disaster is established. Then, the MESS scheduling problem considering the coupling of transportation-distribution networks is transformed into a two-stage optimization problem. The first stage determines the optimal configuration scheme for MESS, and in the second stage, the optimal path selection for MESS can be obtained. Finally, the effectiveness and feasibility of the algorithm proposed in this paper are verified through an improved IEEE-33 node testing system.

A Superhydrophobic Anti-Icing Surface with a Honeycomb Nanopore Structure

Recently, the icing disaster of transmission lines has been a serious threat to the safe operation of the power system. A superhydrophobic (SHP) anti-icing surface with a honeycomb nanopore structure was constructed using anodic oxidation technology combined with a vacuum infusion process. When the current density was 87.5 mA/cm2, the honeycomb porous surface had the best superhydrophobic performance (excellent water mobility), lowest ice-adhesion strength (0.7 kPa) and best anti-frosting performance. Compared with other types of alumina surfaces, the ice-adhesion strength of the SHP surface (87.5 mA/cm2) was only 0.2% of that of the bare surface. The frosting time of the SHP surface (87.5 mA/cm2) was 150 min, which was much slower. The former is attributed to the air cushion within the porous structure and the stress concentration, and the latter is attributed to the self-transition of the droplets and low solid–liquid heat transfer area. After 100 icing or frosting cycles, the SHP surface (87.5 mA/cm2) maintained a low ice-adhesion strength and superhydrophobic performance. This is because the anodic oxidation process forms a hard porous film, and the nano porous structure with a high aspect ratio can store modifiers to realize self-healing. The results indicate that the SHP surface with a honeycomb nanopore structure presents excellent anti-icing performance and durability.

Spatiotemporal distribution characteristics of sea ice disasters in the Northern China Sea from 2001 to 2020

The sea ice disaster is one of the major marine disasters in China. The presence of sea ice poses a potential threat to the social-environmental system in the Northern China Sea during winter. The study focused on investigating the spatiotemporal distribution characteristics of sea ice disasters in the Northern China Sea during 2001–2020, including exploring the variation of sea ice hazards, climate drivers influencing sea ice occurrence, and the subsequent impacts on maritime activity and marine aquaculture. The results show that the sea ice grade in the Bohai and Northern Yellow Seas (BNYS) exhibited a slight downward trend (−0.40/decade, P > 0.10) during the past two decades. The winter temperature and the frequency of cold waves are the primary local climate factors for sea ice variation. The decrease in sea ice grade in the BNYS due to global climate change does not eliminate the occurrence of extreme events. However, the potential for more severe damages remains. It is worth noting that the winters of 2010, 2011, and 2013 stand out as periods with the highest recorded direct economic losses caused by sea ice hazards. In terms of spatial distribution, Liaoning and Shandong provinces have experienced the most severe impacts of sea ice hazards. Sea ice has a negative impact on the throughput of ports that are influenced by sea ice. Significant moderate negative correlations (r = −0.40, P < 0.10) have been observed between port throughput and the sea ice index in Yingkou, Jinzhou, and Tianjin ports. Moreover, the mariculture industry emerged as the primary sector affected by sea ice disasters in the Northern China Sea between 2001 and 2020. Attention should be paid to Haizhou Bay shortly as marine aquaculture in this area has been seriously affected by sea ice in the past five years. In the future, it is crucial to prioritize more accurate and refined monitoring and forecasting of sea ice, enhance the provision of effective icebreaking and pilot services, and optimize the fishery industry structure to prevent sea ice disasters in the Northern China Sea.

Ice breaking by low-velocity impact with a rigid sphere

Penetrator–ice plate impact subjected to dynamic loading constitutes many scientific applications for ice, such as controlling ice disasters by penetrating the ice cover and then entering underwater to explosive ice breaking on Earth, and life detection and resource development under water ice sheet on outer space planets. We present experimental results of brittle damage induced by a small spherical projectile impacting an ice plate with a thickness of 6–50 mm using a drop ball test device or a vertical gas gun. The corresponding impact velocities vary from 2 to 74 m/s. Crater parameters, residual velocity and energy absorption are investigated and assessed. The main fracture patterns and crater formation are captured by two high-speed cameras. A dynamic recrystallization phenomenon of polycrystalline ice is observed, and its formation mechanism is preliminarily analysed. The effects of the impact velocity, ice sample thickness, and restriction conditions on dynamic fracture processes and failure modes are systematically explored. These results improve our understanding of dynamic fracturing mechanism in ice engineering applications.

Comprehensive Evaluation Index System and Application of Low-Carbon Resilience of Power Grid Containing Phase-Shifting Transformer under Ice Disaster

In view of the high impact of extreme disasters, this paper comprehensively evaluates power grid performance from a new low-carbon toughness perspective. First, considering the increase in carbon emissions and the recovery time of carbon emissions, low-carbon resilience indicators are proposed. At the same time, considering the power-regulation effect of the phase-shifter transformer, the fault and response model of a power grid under an ice disaster is established, and then, a comprehensive evaluation index system of low-carbon toughness of the power grid is constructed. The weight determination is carried out using the fuzzy analytic hierarchy process-entropy-based weight method, while the fuzzy comprehensive evaluation center of gravity method is used to evaluate the power grid comprehensively. Finally, examples are presented to verify the feasibility of the proposed method, emphasizing its potential for evaluating the comprehensive performance of low-carbon and toughness of the power grid in the future.

Road use and electrothermal performance of graphene-conductive asphalt-recycled pervious concrete under severe cold environment

Conductive-asphalt-recycled pervious concrete alleviates the urban “heat island effect” and “rain island effect” to realize the resourceful and efficient use of waste concrete and also reduces the probability of urban snow and ice disasters to ensure the safety of winter operation of asphalt pavements in cold areas. This study presents a theoretical and technical basis for ensuring the winter operational safety of asphalt pavements in cold regions. First, we compound graphene and carbon fiber as conductive phase materials and prepare graphene-conductive asphalt-recycled permeable concrete using recycled coarse aggregates with straight top and bottom holes. Then, we analyzed the electrical conductivity, road performance, and electrothermal performance of the specimens prepared using different recycled coarse aggregate substitution rates under different snowfall levels to determine their snow melting efficiency and verify their application in cold regions. The results indicate that the quality of the recycled aggregates is high, the optimal conductive filler ratio is 0.3% polyacrylonitrile-based carbon fiber and 1.5% graphene by mass of asphalt, and the resistivity of the specimens is only 3.2 Ω·m. Moreover, the conductivity and road performance of the graphene-conductive asphalt-recycled concrete decreases with an increase in the replacement rate of the recycled coarse aggregate, and the artificially prefabricated upper and lower through holes affect the conductivity of the specimens to a certain extent. Further, the graphene-conductive asphalt-recycled pervious concrete shows excellent snow melting potential, and as the required snow melting thickness and snow melting time increase, the effect of pervious asphalt slabs for snow melting efficiency becomes more significant. Moreover, when the replacement rate of the recycled concrete aggregate is within the range of 0–50%, it can be completed in 2–3 h under different snowfall levels of snow melting requirements.

Resilient planning for high‐renewable‐integrated transmission systems under the impacts of ice storm disasters

AbstractIce storm disasters pose great challenges to the safe and stable operation of transmission systems, especially in high‐renewable‐integration scenarios. However, as one of the major types of natural disasters that can have severe consequences, ice storm disasters are much less studied than hurricanes and earthquakes. Ice storm disasters are characterized by a long duration and wide scope, which affect the source, network, and load side of the power grid. It is difficult to formulate a quantitative evaluation of resilience and establish effective planning strategies. Here, a resilient planning method considering the interactive effects of ice disasters on sources, networks, and loads is therefore proposed. First, the two‐stage Monte Carlo method is used to generate multiple grid fault scenarios for different levels of ice storm disasters. Then, an index is proposed to quantify the grid resilience as the total amount of system power loss caused by ice storm disasters. Based on this approach, a two‐layer mathematical model is established with grid resilience and total investment cost objectives in each fault scenario. Hierarchical reinforcement and the protection of generating units and transmission lines are selected as investment strategies to mitigate the impact of ice storm disasters. Finally, the IEEE‐24 node transmission system is used to verify the feasibility and effectiveness of the proposed method. Optimizing the planning strategy can significantly improve system resilience with minimal investment cost.

An Early Warning Method of Transmission Line Icing Based on Power Grid Meteorological Monitoring Data

The icing of conductors may seriously affect the safe operation of the power grid, and it is imminent to research the early warning of icing of the power grid. This paper innovatively cites a mesoscale digital-intelligent weather forecast technology considering the influence of micro-topography and combining the power grid meteorological monitoring data to realize the monitoring and early warning of icing on the transmission line of the provincial power grid. The accuracy of the icing forecast can be improved through the interpolation correction of micro-topographic factors and the statistical correction of meteorological monitoring network monitoring. The study has achieved remarkable results in realizing precise line inspections and scientific ice-melting decision-making, enhancing the ability of power grid ice disaster prevention and mitigation, and has important theoretical significance and engineering application value for improving power grid security.

Optimal decision‐making method of transmission lines' maintenance sequence under extreme ice disasters

AbstractProlonged exposure of power transmission lines to extreme ice disasters in the atmosphere disrupts transmission. First, this study establishes a comprehensive failure probability model for the impact of extreme ice disasters on the transmission lines to better understand and improve the transmission lines' ability to withstand such disasters. It predicts the line failure probability based on the initial design data of the lines. Second, the system's weak points are identified, and the fault scenario set is established using the Monte Carlo state sampling method. Next, the system's state is calculated using the resilience assessment index and the DC optimal load reduction model. Finally, an optimal decision‐making method for the transmission line maintenance sequence is proposed from the post‐disaster maintenance scheduling perspective. Considering the travel time and maintenance effect, this method can effectively restore the system state in response to extreme ice disasters. The IEEE 30‐bus power system is taken as an example of simulation verification. The results show that this method can effectively complete the system load state restoration and improve the transmission system's resilience. It also has certain practicality and good practical application values.