Cold Regions Science and Technology最新文献_第3页

Residual stress evolution during ice accretion from a single water droplet 单水滴结冰过程中的残余应力演化

IF 3.8 2区工程技术 Q1 ENGINEERING, CIVIL

Cold Regions Science and Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-08 DOI: 10.1016/j.coldregions.2026.104824

Motoki Sakaguchi , Kanta Ishida , Koichiro Tanaka , Moeka Tsukamoto , Chao Kang , Yu Kurokawa

Ice accretion on solid surfaces at low temperatures causes serious problems in numerous engineering applications, such as aircraft, wind turbines, and power lines. To develop effective anti-icing and de-icing technologies, understanding the mechanism of residual stress evolution within the ice film during the accretion process is essential. In this study, experiments were conducted to clarify the residual stress evolution during the solidification of a single water droplet dropped onto a low-temperature stainless steel substrate. In-situ observations of the droplet impact, spreading, and solidification were performed, varying the substrate temperature as a key parameter. Simultaneously, strain was measured on the substrate's backside using a strain gauge, showing that tensile strain develops on the substrate as the droplet solidifies. Furthermore, the residual strain increased with the decreasing substrate temperature. Cracking occurred immediately at lower substrate temperatures, whereas higher temperatures required additional cooling for cracking. Numerical analysis reproduced these experimental observations, quantifying the stress evolution within the ice film. Temperature-dependent material properties were incorporated in the analysis, as well as a creep constitutive equation based on stress relaxation tests, thereby capturing the time-dependent residual strain on the substrate and elucidating the distribution and evolution of tensile residual stress within the ice film. Ultimately, tensile stress developed during solidification and increased with cooling, reaching approximately 7–8 MPa at the time of cracking. These findings provide a fundamental understanding of the thermo-mechanical processes during ice accretion, crucial for developing robust anti-icing and de-icing strategies.

低温下固体表面上的冰积聚在许多工程应用中引起了严重的问题，如飞机、风力涡轮机和电力线。为了开发有效的防冰和除冰技术，了解冰膜内残余应力的演化机制至关重要。在本研究中，通过实验来阐明单个水滴在低温不锈钢基体上凝固过程中的残余应力演变。在改变基体温度作为关键参数的情况下，进行了液滴撞击、扩散和凝固的现场观察。同时，用应变计测量了基底背面的应变，结果表明，随着液滴凝固，基底上产生了拉伸应变。残余应变随基体温度的降低而增大。在较低的基材温度下，裂缝立即发生，而较高的温度下，裂缝需要额外的冷却。数值分析再现了这些实验观察，量化了冰膜内的应力演化。分析中纳入了与温度相关的材料特性，以及基于应力松弛试验的蠕变本构方程，从而捕获了基材上与时间相关的残余应变，并阐明了冰膜内拉伸残余应力的分布和演变。最终，拉伸应力在凝固过程中产生，并随着冷却而增大，在开裂时达到约7-8 MPa。这些发现提供了对冰增生过程热力过程的基本理解，对于制定强有力的防冰和除冰策略至关重要。

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引用次数: 0

Kilometer-scale simulation of atmospheric in-cloud ground icing over complex terrain in Norwegian Arctic 挪威北极复杂地形上大气云中地面结冰的千米尺度模拟

IF 3.8 2区工程技术 Q1 ENGINEERING, CIVIL

Cold Regions Science and Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-29 DOI: 10.1016/j.coldregions.2026.104851

Pravin Punde , Yngve Birkelund , Muhammad Shakeel Virk , Xingbo Han , Trude Eidhammer

This study investigates in-cloud ground icing over Fagernesfjellet, a complex terrain site in northern Norway, using new kilometer-scale simulations from the Weather Research and Forecasting model (WRF) during the year 2022–2023. The WRF-derived icing results and meteorological parameters relevant to in-cloud icing are validated using measurement data (MEAS), with focus on model resolution and terrain height. Our findings indicate that WRF effectively represented the temporal evolution of icing events, with higher altitudes indicating more severe icing and an increased number of annual icing hours. However, in the current model setup, WRF underestimates the magnitude and variability of icing loads; an improvement in icing load amount is found when accurate terrain height point is used. The highest spatial resolution improved the simulation of key meteorological parameters, such as temperature and wind speed, but struggled with relative humidity and wind direction. Our study shows that high-resolution simulation and accurate terrain height are essential for improving atmospheric in-cloud ground icing simulations over complex terrain.

本研究利用气象研究与预报模型（WRF）在2022-2023年期间的新千米尺度模拟，调查了挪威北部复杂地形Fagernesfjellet的云内地面结冰情况。利用测量数据（MEAS）验证了wrf导出的结冰结果和与云内结冰相关的气象参数，重点是模型分辨率和地形高度。研究结果表明，WRF有效地反映了结冰事件的时间演变，海拔越高，结冰越严重，年结冰小时数越长。然而，在目前的模型设置中，WRF低估了结冰载荷的大小和变异性；采用精确的地形高度点可以提高结冰载荷。最高空间分辨率提高了对温度、风速等关键气象参数的模拟，但对相对湿度和风向的模拟效果较差。我们的研究表明，高分辨率的模拟和精确的地形高度对于改善复杂地形下的大气云内地面结冰模拟至关重要。

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引用次数: 0

Integrating convolutional neural networks and explainable AI for enhanced winter road surface conditions classification using stationary RWIS imagery 整合卷积神经网络和可解释的人工智能，使用静止RWIS图像增强冬季路面状况分类

IF 3.8 2区工程技术 Q1 ENGINEERING, CIVIL

Cold Regions Science and Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-19 DOI: 10.1016/j.coldregions.2026.104832

Yong Wook Lee , Mingjian Wu , Tae J. Kwon

Adverse winter weather significantly compromises driving safety and mobility in regions such as Canada and the northern United States. This study addresses these challenges by utilizing stationary Road Weather Information Systems (RWIS) equipped with cameras. These images capture complex scenes, making automated road surface condition (RSC) classification systems particularly challenging. Unlike previous studies that required manual cropping of main road pavement, we applied convolutional neural networks (CNNs) directly to full stationary RWIS imagery to validate their effectiveness and generalizability for real-world winter road maintenance (WRM) applications. Our study focused on four key aspects: (1) rigorously validating CNN performance on stationary RWIS images without manual cropping, (2) systematically analyzing the influence of camera angles using explainable artificial intelligence (XAI) techniques, (3) evaluating the effect of image resolution on model accuracy, and (4) exploring data-quantity trade-offs, including the impact of adding or removing camera feeds, to develop robust and deployable CNN models. The developed CNN achieved excellent performance metrics, all exceeding 98%. Our findings indicate that optimizing camera orientation substantially enhances the model's focus on relevant features that align with human interpretation. Reducing background complexity and increasing road captures from different perspectives further enhanced model focus. Furthermore, increasing image resolution up to 224 × 224 improved performance, although gains were marginal beyond this point while computational costs rose substantially. This comprehensive evaluation demonstrates the high potential of using stationary RWIS imagery for RSC classification with CNNs, suggesting significant improvements in WRM efficiency and traffic safety during winter.

在加拿大和美国北部等地区，恶劣的冬季天气严重影响了驾驶安全和机动性。本研究通过使用配备摄像头的固定式道路天气信息系统（RWIS）来解决这些挑战。这些图像捕获了复杂的场景，使得自动路面状况（RSC）分类系统尤其具有挑战性。与之前需要人工裁剪主干道路面的研究不同，我们将卷积神经网络（cnn）直接应用于完全静止的RWIS图像，以验证其在现实世界冬季道路维护（WRM）应用中的有效性和泛化性。我们的研究集中在四个关键方面：(1)严格验证CNN在静止RWIS图像上无需手动裁剪的性能；(2)使用可解释的人工智能（XAI）技术系统地分析相机角度的影响；(3)评估图像分辨率对模型精度的影响；(4)探索数据量权衡，包括添加或删除相机馈源的影响，以开发鲁棒和可部署的CNN模型。开发的CNN取得了优异的性能指标，均超过98%。我们的研究结果表明，优化相机方向大大增强了模型对与人类解释一致的相关特征的关注。降低背景复杂性和增加从不同角度捕获的道路进一步增强了模型的焦点。此外，将图像分辨率提高到224 × 224可以提高性能，尽管在计算成本大幅增加的情况下，超出这一点的收益微乎其微。这项综合评价表明，使用固定RWIS图像与cnn一起进行RSC分类具有很高的潜力，这表明冬季WRM效率和交通安全得到了显著提高。

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引用次数: 0

Study on shear strength prediction model of glass fiber-improved loess in seasonal frozen regions based on POA-XGBoost 基于POA-XGBoost的季节冻土区玻璃纤维改性黄土抗剪强度预测模型研究

IF 3.8 2区工程技术 Q1 ENGINEERING, CIVIL

Cold Regions Science and Technology

Pub Date : 2026-04-01 Epub Date: 2026-02-08 DOI: 10.1016/j.coldregions.2026.104857

Xiyue Wang , Wanjun Ye , Qianqian Ma , Hao Yang , Minghao Zhang

In seasonal frozen regions, loess is susceptible to mechanical damage under the action of freeze-thaw (F-T) cycles and moisture content changes. Fiber reinforcement is recognized as an effective technique for enhancing its engineering performance. As a critical index for assessing fiber-reinforced loess, shear strength is governed by multiple interactive factors, including the number of F-T cycles, fiber content, and confining pressure, rendering its accurate prediction challenging. To address this challenge, a series of triaxial shear tests on fiber-reinforced loess subjected to wet-dry freeze-thaw (WDFT) cycles was performed. The influence mechanisms of cycle number, fiber content, and confining pressure on shear strength indicators were revealed through Spearman correlation analysis. On this basis, to construct a high-precision prediction model, multiple algorithms including support vector regression (SVR), random forest (RF), and generalized additive model (GAM) were compared. Extreme gradient boosting (XGBoost) was finally selected as the benchmark model. Subsequently, the pelican optimization algorithm (POA) was introduced for automatic hyperparameter optimization of the model. A hybrid machine learning model (POA-XGBoost) was thus proposed. The results indicate that the POA-XGBoost model achieved superior predictive performance. On the test set, the model attained a coefficient of determination (R²) of 0.9767 and a mean absolute percentage error (MAPE) of 0.0406. Compared with the benchmark XGBoost model, this represents an improvement in prediction accuracy exceeding 32%. This study provides a reliable and novel tool for predicting the mechanical properties of fiber-reinforced loess in seasonal frozen regions.

在季节性冻土区，黄土在冻融循环和含水率变化的作用下易发生机械损伤。纤维加固是提高其工程性能的有效技术。作为评价纤维增强黄土抗剪强度的重要指标，抗剪强度受F-T循环次数、纤维含量和围压等多种交互因素的影响，其准确预测具有一定的挑战性。为了解决这一挑战，对纤维增强黄土进行了干湿冻融循环（WDFT）的一系列三轴剪切试验。通过Spearman相关分析揭示了循环次数、纤维含量和围压对抗剪强度指标的影响机理。在此基础上，对支持向量回归（SVR）、随机森林（RF）和广义加性模型（GAM）等算法进行比较，构建高精度预测模型。最终选择极限梯度提升（Extreme gradient boost, XGBoost）作为基准模型。随后，引入了鹈鹕优化算法（pelican optimization algorithm， POA）对模型进行超参数自动优化。提出了一种混合机器学习模型（POA-XGBoost）。结果表明，POA-XGBoost模型具有较好的预测性能。在测试集上，模型的决定系数（R2）为0.9767，平均绝对百分比误差（MAPE）为0.0406。与基准XGBoost模型相比，该模型的预测精度提高了32%以上。该研究为季节性冻土区纤维增强黄土的力学特性预测提供了可靠的新工具。

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引用次数: 0

New insight into mechanical evolution and micro-mechanisms of early-age frozen engineered cementitious composites 早期冻结工程胶凝复合材料力学演化与微观机制新认识

IF 3.8 2区工程技术 Q1 ENGINEERING, CIVIL

Cold Regions Science and Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-15 DOI: 10.1016/j.coldregions.2026.104830

Huayang Sun , Yanlin Huo , Xiaobing Ma , Xuesi Ji , Zhichao Xu , Zhitao Chen , Yingzi Yang

Early-age frozen damage poses significant challenges to concrete durability and structural integrity in cold-region construction. This study investigates the long-term mechanical properties of early-age frozen sulphoaluminate cement-engineered cementitious composites (SAC-ECC). Mechanical tests, single-fiber pullout tests, micromechanical modeling, and microstructural analyses were conducted to evaluate the effects of pre-curing times and frozen temperatures. The experimental results demonstrated that SAC-ECC with shorter pre-curing times (0.75 h), both compressive and flexural strengths increased as frozen temperatures decreased from 0 °C to −10 °C. In contrast, for SAC-ECC with longer pre-curing times (1.5 h and 3 h), the compressive and flexural strengths were aggravated with decreasing freezing temperatures. In addition, longer pre-curing times and lower frozen temperatures reduced tensile strength but significantly enhanced tensile ductility. For SAC-ECC frozen at −10 °C after 3 h of pre-curing, the tensile strain increased by 92.94% compared with the unfrozen group. Micromechanical and microstructural analyses revealed that for early-frozen SAC-ECC, a shorter pre-curing time improved the pore structure and fiber matrix interface, whereas a longer pre-curing time increased porosity and weakened interfacial bonding. The TOPSIS analysis can effectively balance the mechanical properties and time cost, thus providing valuable guidance for the application of SAC-ECC in cold-region construction.

在寒区施工中，早期冻害对混凝土耐久性和结构完整性提出了重大挑战。研究了早期冷冻硫铝酸盐水泥-工程胶凝复合材料（SAC-ECC）的长期力学性能。通过力学试验、单纤维拉伸试验、微观力学建模和微观结构分析来评估预固化时间和冷冻温度的影响。实验结果表明，SAC-ECC预固化时间较短（0.75 h），其抗压强度和抗弯强度随冻结温度从0℃降至- 10℃而增加。而预养护时间较长的SAC-ECC (1.5 h和3 h)，其抗压强度和抗弯强度随冻结温度的降低而增大。此外，较长的预固化时间和较低的冷冻温度降低了拉伸强度，但显著提高了拉伸延展性。预固化3 h后- 10℃冻结的SAC-ECC拉伸应变较未冻结组提高了92.94%。微观力学和微观结构分析表明，对于早冻的SAC-ECC，较短的预固化时间改善了孔隙结构和纤维基质界面，而较长的预固化时间增加了孔隙率，减弱了界面结合。TOPSIS分析可以有效地平衡力学性能和时间成本，为SAC-ECC在寒区施工中的应用提供了有价值的指导。

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引用次数: 0

Can Sentinel-1 reliably provide regional-scale information on avalanche activity 哨兵-1能否可靠地提供雪崩活动的区域尺度信息

IF 3.8 2区工程技术 Q1 ENGINEERING, CIVIL

Cold Regions Science and Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1016/j.coldregions.2026.104822

Suvrat Kaushik , Fatima Karbou , Nicolas Eckert , Léo Viallon-Galinier , Adrien Mauss

Snow avalanches pose a major hazard in mountainous regions, but their sporadic occurrence and remote locations hinder consistent regional monitoring. Automated remote sensing techniques, particularly those using Synthetic Aperture Radar (SAR), offer promising solutions for systematic data collection. However, validating SAR-based avalanche detections remains challenging due to the limited availability of ground truth data, spatial mismatches, temporal inconsistencies between reference datasets, and uncertainties associated with the relatively simple hypotheses underlying detection algorithms. This study assesses the performance and reliability of SAR-based avalanche debris detection across seven massifs in the French Alps over two winter seasons (2017–2018 and early 2020). The SAR-derived detections are evaluated against multiple indicators of avalanche activity, including avalanche inventories, snow cover simulation models, and hazard levels from official French avalanche bulletins. The findings demonstrate that, overall, SAR-based methods effectively capture the spatial and temporal patterns of ground-observed avalanche activity and align well with reported hazard levels, particularly during periods of elevated avalanche risk. Notably, for the Beaufortain massif during the 2020 season, SAR detections achieved a Pearson correlation coefficient of 0.65 with ground-based observations. Nevertheless, performance varies significantly across massifs and seasons, with strong correlations in some areas and weaker associations in others. The topographic characteristics (slope, elevation, aspect) of detected debris also show good agreement with other indicators. Despite inherent biases in each reference dataset, the results highlight the potential of SAR imagery for capturing regional-scale spatiotemporal dynamics of avalanches. While SAR offers valuable insights, detection remains far from perfect, underscoring the continued need for direct field observations and further refinement of detection algorithms to improve accuracy and validation.

雪崩是山区的主要灾害，但其零星发生和偏远地区阻碍了持续的区域监测。自动化遥感技术，特别是使用合成孔径雷达（SAR）的技术，为系统数据收集提供了有前途的解决方案。然而，由于地面真实数据的可用性有限、参考数据集之间的空间不匹配、时间不一致以及与检测算法相关的相对简单假设相关的不确定性，验证基于sar的雪崩检测仍然具有挑战性。本研究评估了两个冬季（2017-2018年和2020年初）法国阿尔卑斯山七个地块的基于sar的雪崩碎片检测的性能和可靠性。根据雪崩活动的多种指标，包括雪崩清单、积雪模拟模型和来自法国官方雪崩公报的危险级别，对sar衍生的探测结果进行评估。研究结果表明，总体而言，基于sar的方法有效地捕获了地面观测到的雪崩活动的时空模式，并与报告的危险水平很好地吻合，特别是在雪崩风险升高的时期。值得注意的是，对于2020年季节的Beaufortain地块，SAR探测与地面观测的Pearson相关系数为0.65。然而，不同地块和季节的表现差异很大，在某些区域具有很强的相关性，而在其他区域具有较弱的相关性。探测到的碎屑的地形特征（坡度、高程、坡向）也与其他指标吻合较好。尽管每个参考数据集都存在固有的偏差，但研究结果强调了SAR图像在捕捉区域尺度的雪崩时空动态方面的潜力。虽然SAR提供了有价值的见解，但检测还远远不够完美，因此需要继续进行直接的现场观测，并进一步改进检测算法，以提高准确性和有效性。

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引用次数: 0

Image-based ice shape and accretion process prediction on wind turbine blades via deep learning with curvature consistency constraint 基于曲率一致性约束的深度学习图像风力机叶片冰形和吸积过程预测

IF 3.8 2区工程技术 Q1 ENGINEERING, CIVIL

Cold Regions Science and Technology

Pub Date : 2026-04-01 Epub Date: 2026-02-02 DOI: 10.1016/j.coldregions.2026.104853

Jianing Pan , Xin Zhang , Yibo Xi , Kuigeng Lin , Zhenyu Wang

Blade icing poses a major threat to the operational reliability and power generation of wind turbines in cold regions. To overcome the limitations of high computation demand and complicated modelling procedures in blade icing prediction, this study proposes an image-based deep learning model for efficient prediction of ice shape and accretion process. Firstly, systematic computational fluid dynamics (CFD)-based icing simulations were conducted on the blade of a 5 MW onshore wind turbine to investigate the effects of environmental parameters on ice accretion. The results revealed distinct characteristics of typical rime ice and glaze ice. A SegFormer-based model was developed for ice shape prediction, achieving superior performance compared with other models. This model was further enhanced by introducing a novel curvature consistency-based loss function, which significantly improved the geometric fidelity of predicted ice shapes, especially for fine-grained features such as sharp protrusions. Comprehensive evaluations verified the proposed model's robustness under varied environmental conditions, dynamic ice accretion processes, and different airfoil geometries. Furthermore, the predicted ice shapes of individual blade elements were integrated to reconstruct the full three-dimensional ice morphology and estimate the total ice mass across the entire blade. This study establishes a robust and efficient framework for ice shape prediction, which lays a solid foundation for the subsequent precise aerodynamic performance analysis of iced wind turbine blades.

在寒冷地区，叶片结冰对风力发电机的运行可靠性和发电量构成了重大威胁。针对叶片结冰预测计算量大、建模过程复杂的局限性，提出了一种基于图像的深度学习模型，可有效预测冰形和冰积过程。首先，基于系统计算流体力学（CFD）对某5mw陆上风力发电机组叶片结冰进行了模拟，研究了环境参数对结冰的影响。结果表明，典型的霜冰和釉冰具有明显的特征。建立了基于segformer的冰形预测模型，取得了较好的预测效果。通过引入一种新的基于曲率一致性的损失函数，该模型得到了进一步增强，显著提高了预测冰形的几何保真度，特别是对于尖锐突出等细粒度特征。综合评估验证了该模型在不同环境条件、动态冰积过程和不同翼型几何形状下的鲁棒性。在此基础上，将叶片各单元的预测冰形进行整合，重建叶片的三维冰形态，并估算整个叶片的总冰质量。本研究建立了一个稳健高效的冰形预测框架，为后续对结冰风力机叶片进行精确气动性能分析奠定了坚实的基础。

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引用次数: 0

A laboratory-based spectrometer intercomparison for the measurement of snow spectra 基于实验室的雪光谱测量光谱仪比对

IF 3.8 2区工程技术 Q1 ENGINEERING, CIVIL

Cold Regions Science and Technology

Pub Date : 2026-04-01 Epub Date: 2025-12-22 DOI: 10.1016/j.coldregions.2025.104800

Benjamin M. Roberts-Pierel , Christopher J. Crawford , Steven W. Brown , Raymond F. Kokaly , Kelly E. Gleason , Anne W. Nolin , Edward H. Bair , Brenton A. Wilder , Anton J. Surunis , S. McKenzie K. Skiles , Joachim Meyer , Allyson E. Fitts , Jeremy M. Johnston , Adam G. Hunsaker , Martin Stuefer , Trond Løke

Seasonal snow is an integral component of global hydrological systems, global energy budget and Earth's climate. As an important part of many Earth systems, seasonal snow is also an essential source of water for many human populations and ecosystems around the world. As such, the measurement of seasonal snow and characterization of uncertainty in those measurements is crucial. To elucidate potential uncertainty attributable to commonly used field spectrometers (and to a lesser extent imaging spectrometers) and associated reference panels, this work presents results from an intercalibration experiment conducted synchronously with the NASA 2023 Snow Experiment (SnowEx) Albedo campaign near Fairbanks, Alaska USA. Three sets of experiments were carried out under controlled laboratory conditions to characterize the radiometric and spectral wavelength consistency of the instruments as well as the white reference panels used to calculate reflectance from field measurements. Although there was generally good agreement between the instruments, panels, and the references, there were also some notable differences. One instrument showed an average − 74 % change from the reference for radiance, and multiple instruments exceeded the suggested 0.5 nm threshold for spectral wavelength scale. The Discussion section highlights how some of these findings and their implications could improve future field campaigns and general use/maintenance of these high-precision scientific instruments.

季节性降雪是全球水文系统、全球能源收支和地球气候的重要组成部分。作为许多地球系统的重要组成部分，季节性雪也是世界各地许多人口和生态系统的重要水源。因此，季节性降雪的测量和这些测量中的不确定性的表征是至关重要的。为了阐明常用的野外光谱仪（以及较小程度上的成像光谱仪）和相关参考面板的潜在不确定性，本工作介绍了与美国阿拉斯加州费尔班克斯附近的NASA 2023雪实验（SnowEx）反照率运动同步进行的相互校准实验的结果。在受控的实验室条件下进行了三组实验，以表征仪器的辐射和光谱波长一致性，以及用于计算现场测量反射率的白色参考板。虽然在文书、小组和参考文献之间普遍存在良好的一致性，但也存在一些显著的差异。一台仪器的辐射值比参考值平均变化了- 74%，多台仪器的光谱波长范围超过了建议的0.5 nm阈值。讨论部分强调了这些发现及其影响如何能够改善未来的实地活动和这些高精度科学仪器的一般使用/维护。

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引用次数: 0

Rapid retreat of tropical glaciers in Puncak Jaya, Papua: Four decades of change observed from Landsat Imagery, 1980–2024 巴布亚省punak Jaya热带冰川的快速退缩：从陆地卫星图像观察到的四十年变化，1980-2024

IF 3.8 2区工程技术 Q1 ENGINEERING, CIVIL

Cold Regions Science and Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-17 DOI: 10.1016/j.coldregions.2026.104831

Francine Hematang , Jonni Marwa , Anton Sinery , Meliza Worabai , Dominggas Renwarin , Evelin Tanur , Obed Lense , Dina Arung Padang , Alexander Rumatora , Elieser Sirami , Ana Tampang , Christian Imburi , Petrus Dimara

Climate change continues to occur, so that the chrysosphere ecosystem has been heavily affected. This study aimed to investigate changes of glacier area in Puncak Jaya in response to ongoing global climate change issue. Landsat 2–9 satellite image series was used to see the dynamics of glacier change using digitized on-screen method. An important finding is that the tropical glaciers of Papua will survive until 2024, but melt and disappear faster than researchers previously predicted. Results showed that the glacier area has decreased by 7.28 km² (97%) in over 44 years. In 1980, Puncak Jaya and Idenburg glaciers covered 7.46 km² and then decreased to 0.19 km² in 2024. Research also shows that only two glaciers remain, while four others Ngga Pilimsit Glacier, Meren Glacier, Southwall Hanging Glacier and West Northwall Firn Glacier have disappeared. In 2024, the Carstensz Glacier covered 0.050 km² and the East Firn Northwall Glacier 0.136 km². Another important finding is that the East Firn Northwall Glacier is predicted to disappear faster in 2028–2029 than the Carstensz Glacier in 2029–2030. In the future, it will be necessary to collect all the information from the field, estimate the impact on the ecosystem if the glacier completely disappears, and find out why East Northwall Firn Glacier melts faster than Carstensz Glacier.

气候变化不断发生，使温圈生态系统受到严重影响。本研究旨在探讨punak Jaya冰川面积的变化对全球气候变化问题的响应。利用Landsat 2-9卫星图像序列，采用数字化屏幕显示方法观察冰川变化动态。一项重要的发现是，巴布亚的热带冰川将存活到2024年，但融化和消失的速度比研究人员先前预测的要快。结果表明：44年来冰川面积减少了7.28 km2 (97%)；1980年，Puncak Jaya和Idenburg冰川覆盖面积为7.46 km2, 2024年减少至0.19 km2。研究还表明，只有两个冰川仍然存在，而其他四个冰川，阿嘎皮利姆斯特冰川，梅伦冰川，南墙悬挂冰川和西北墙芬冰川已经消失。2024年，Carstensz冰川覆盖面积为0.050 km2， East Firn Northwall冰川覆盖面积为0.136 km2。另一个重要的发现是，预计2028-2029年东芬-诺斯沃尔冰川的消失速度将超过2029-2030年卡斯滕斯冰川的消失速度。在未来，有必要收集所有来自现场的信息，估计冰川完全消失对生态系统的影响，并找出为什么东北壁芬冰川比卡斯滕斯冰川融化得更快。

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引用次数: 0

Research on transmission line icing classification and recognition algorithm based on BiTex-ResNet34 基于BiTex-ResNet34的输电线路结冰分类识别算法研究

IF 3.8 2区工程技术 Q1 ENGINEERING, CIVIL

Cold Regions Science and Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-23 DOI: 10.1016/j.coldregions.2026.104845

Ye Zhang, Yufeng Zhan, Hongru Chen, Yongcan Zhu, Long Zhao, Yi Tian

The transmission line icing scenario is complex, with limited samples for different icing types and high sim-ilarity between them. Moreover, there is currently a lack of non-contact methods for identifying icing types. Therefore, this paper proposes a transmission line icing classification algorithm based on BiTex-ResNet34. Firstly, a quantitative analysis of the texture features of icing images is conducted to identify the most significant feature parameters that highlight the mean differences between different icing types, thereby enhancing the discriminability between them. Secondly, the model adopts a dual-branch architecture, with each branch containing a complete ResNet34 convolutional backbone network to parallelly extract both the raw features and texture features of icing images, thereby enhancing the model's feature representation. Finally, the Second-order Feature Fusion Module (SK-FM) module is embedded at different layers of the model's dual-branch architecture. This module integrates second-order features and concatenates the Selective Kernel (SK) attention mechanism to capture the correlations between different icing feature information, thereby improving the model's ability to distinguish between three types of icing: rime, hard rime, and soft rime. Experimental results show that BiTex-ResNet34 can accurately identify the three types of icing—glaze, hard rime, and soft rime—under complex environments, achieving precision, recall, and F1-score of 94.7%, 91.07%, and 92.85%, respectively, providing a new approach for transmission line icing type recognition.

输电线路结冰场景复杂，不同结冰类型的样本有限，且相似度高。此外，目前还缺乏识别结冰类型的非接触方法。为此，本文提出了一种基于BiTex-ResNet34的传输线结冰分类算法。首先，对结冰图像的纹理特征进行定量分析，找出最显著的特征参数，突出不同结冰类型之间的平均差异，从而增强它们之间的区分能力。其次，模型采用双分支架构，每个分支包含一个完整的ResNet34卷积主干网络，并行提取结冰图像的原始特征和纹理特征，增强模型的特征表示。最后，二阶特征融合模块（SK-FM）模块被嵌入到模型的双分支架构的不同层。该模块集成了二阶特征，并连接了选择性核（SK）注意机制，捕捉不同结冰特征信息之间的相关性，从而提高了模型区分霜、硬霜和软霜三种结冰类型的能力。实验结果表明，在复杂环境下，BiTex-ResNet34能够准确识别出釉霜、硬霜和软霜三种类型的冰霜，准确率、召回率和评分分别达到94.7%、91.07%和92.85%，为输电线路冰霜类型识别提供了一种新的方法。

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引用次数: 0