Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study invest...Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study investigates their coupled effects using inclinedflume experiments and Discrete Element Method(DEM)simulations,covering three gravel sizes(2-5 mm,5-7 mm,7-10 mm)and four ice-content levels(0%,20%,40%,60%).Run-out distance,velocity,energy components,flow regime(Savage number),and segregation indexαwere quantified.Increasing ice content significantly enhances mobility,but with diminishing marginal effectiveness.From 0%to 40%ice content,run-out distance increases by 41%-86%,whereas the additional increase from 40%to 60%contributes only 12%-23%.Particle-size ratio strongly governs segregation intensity.Fine-gravel groups reach segregation indices ofα=0.92-0.98,indicating nearly complete upward migration of ice,whereas medium-gravel and coarse-gravel groups exhibit much weaker segregation,stabilizing atα=0.68-0.74 and 0.60-0.69.Savage number analyses reveal marked flow-regime transitions.At 0%ice content,Savage numbers reach 1.0-1.5,indicating a collisional regime.Increasing ice content suppresses collisionality,with Savage numbers decreasing to 0.03-0.07 at 60%ice content,consistent with dense-regime flow.DEM energy analyses confirm this regime shift:for finegravel mixtures,collision energy decreases by 14%,while sliding-friction energy increases by 33%as ice content increases from 0%to 60%,reflecting enhanced overburden effects imposed by upward-segregated ice layers.Medium and coarse mixtures exhibit weaker or opposite energy-shift patterns,demonstrating strong size dependence.Mechanistically,large particle-size contrasts promote strong segregation and form dense basal rock layers that increase basal friction and reduce mobility.When particle sizes are similar or ice content is high,segregation remains limited,allowing ice to mix into the basal layer,thereby reducing basal friction and enhancing mobility.This research quantitatively demonstrates how composition controls particle spatial distribution,flow regime,and energy dissipation,offering new mechanistic insights into the propagation and deposition behaviors of rock-ice avalanches and improving hazard assessment in vulnerable high-mountain regions.展开更多
准确评估雪层体积含水量和体积密度对于理解雪的水文过程、降低雪崩风险以及冰冻圈监测具有重要意义。本文提出一种新型双参数反演框架,该框架集成了合成电磁建模、降维方法和机器学习算法,用于从探地雷达(GPR)数据中提取相对介电常数...准确评估雪层体积含水量和体积密度对于理解雪的水文过程、降低雪崩风险以及冰冻圈监测具有重要意义。本文提出一种新型双参数反演框架,该框架集成了合成电磁建模、降维方法和机器学习算法,用于从探地雷达(GPR)数据中提取相对介电常数和对数电阻率。传统雪层测量方法具有侵入性、劳动强度大且仅限于点位观测等局限性。为克服上述局限,建立了一种非侵入性、可扩展且数据驱动的框架,利用合成GPR数据集来表示具有不同含水量和密度分布的多样化雪层条件。使用先进电磁模拟器gprMax,通过有限差分时域模拟生成合成的一维时序反射(A扫描)。随后采用主成分分析(PCA)将每个A扫描进行压缩,得到低维且信息保真的特征集,从而显著提升模型训练效率。基于经过主成分分析(PCA)降维处理的特征集,训练了随机森林、神经网络、支持向量机和极限梯度提升四种机器学习模型。其中,神经网络模型性能最佳,介电常数为R^(2)>0.97,电阻率为R^(2)>0.92。合成数据中引入高斯噪声(信噪比约为6 dB),并通过针对特定领域进行改进,以提高其在实地数据的泛化能力。模型在中国阿尔泰山脉的两条典型GPR剖面(湿雪T750和饱和雪G125)上进行了验证。神经网络模型预测结果与GPR反演、Snowfork测量及人工雪坑数据高度一致,体积含水量偏差不超过1.5%,体积密度误差在30–84 kg m-3范围内。结果表明,基于机器学习的反演方法在结合真实模拟与数据增强的条件下,能够实现可扩展、非侵入式的雪层特性反演,在水文预报、雪情监测及气候敏感型水资源管理中具有重要应用价值。展开更多
本文对2023年12月旅顺出现的连续10天强降雪天气事件进行回顾,运用多个站点观测资料、ERA5的位势高度场、海表温度场、中央气象台的天气形势图与近10年12月连续降雪事件加以对比总结,结果表明:2023年12月平均的冷涡强度最强(中心强度为5...本文对2023年12月旅顺出现的连续10天强降雪天气事件进行回顾,运用多个站点观测资料、ERA5的位势高度场、海表温度场、中央气象台的天气形势图与近10年12月连续降雪事件加以对比总结,结果表明:2023年12月平均的冷涡强度最强(中心强度为5080位势米),强大的冷涡以及极为偏东的高空冷空气堆积是此次连续降雪事件的主要系统;渤海海峡的等温线分布有一个明显的向渤海以北延伸的暖舌(5.2℃~7.6℃之间),平均海气温差在9.4℃,这样冷空气在经过上游暖舌区域增温增湿后可给辽东半岛带来冷流降雪;此次连续降雪既有系统性降雪,又有冷流性降雪,其中冷流降雪(旅顺地区风速小),850 hpa或700 hpa分别处在涡后或槽后脊前,系统性降雪,500 hpa、700 hpa我区均处于高空槽前,850 hpa处于槽后脊前;冷流降雪前及过程中,辽东半岛北部中低空都出现了明显的垂直运动、水汽输送、水汽辐合,700 hpa相较850 hpa更为明显。In this paper, the 10 consecutive days of heavy snowfall in Lvshun in December 2023 was reviewed, and the observation data of several stations, the geopotential height field of ERA5, the sea surface temperature field, and the weather situation chart of the National Meteorological Center of CMA were compared with the continuous snowfall events in December in recent 10 years. The results show that: In December 2023, the average intensity of the cold vortex is the strongest (the central intensity is 5080 geopotential meters), and the strong cold vortex and the accumulation of high altitude cold air very far to the east are the main systems of this continuous snowfall event. The isothermal distribution of the strait has an obvious warm tongue (between 5.2˚C~7.6˚C) extending to the north of the Bohai Sea, the average temperature difference between the sea and the air is 9.4˚C, so that the cold air can bring cold flow snow to the Liaodong Peninsula after warming and humidification in the upstream warm tongue area. The continuous snowfall has both systematic snowfall and cold flow snowfall, among which cold flow snowfall (low wind speed in the Lvshun area), 850 hpa or 700 hpa are behind the vortex or in front of the rear ridge of the trough, and systematic snowfall, 500 hpa and 700 hpa are in front of the upper trough, and 850 hpa is in front of the rear ridge of the trough. Before and during the cold current snowfall, there are obvious vertical movements, water vapor transport and water vapor convergence in the middle and low levels in the northern part of the Liaodong Peninsula, the phenomenon is more pronounced at 700 hpa than 850 hpa.展开更多
Accurate assessment of snowpack volumetric liquid water content and bulk density is essential for understanding snow hydrology,avalanche risk management,and monitoring cryosphere changes.This study presents a novel du...Accurate assessment of snowpack volumetric liquid water content and bulk density is essential for understanding snow hydrology,avalanche risk management,and monitoring cryosphere changes.This study presents a novel dual-parameter inversion framework that integrates synthetic electromagnetic modelling,dimensionality reduction,and machine learning algorithms to extract relative permittivity and log-resistivity from ground-penetrating radar(GPR)data.Traditional snowpack measurements are invasive,labor-intensive,and limited to point observations.To overcome these limitations,we developed a non-invasive,scalable,and data-driven framework that uses synthetic GPR datasets representing diverse snowpack conditions with variable moisture and density profiles.Synthetic 1D time series reflections(A-scans)are generated using finite-difference time-domain simulations in the state-of-the-art electromagnetic simulator gprMax.Principal component analysis(PCA)is applied to compress each A-scan while preserving key features,which significantly improved and enhanced the model training efficiency.Four machine learning models,including random forest,neural network,support vector machine,and eXtreme gradient boosting,are trained on PCA-reduced features.Among these,the neural network model achieved the best performance,with R^(2)>0.97 for permittivity and R 2>0.92 for resistivity.Gaussian noise(signal-to-noise ratio of 6 dB)is introduced to the synthetic data,and then targeted domain adaptation is employed to enhance generalization to field data.The framework is validated on two contrasting GPR transects in the Altay Mountains of the Chinese mainland,representing moist(T750)and wet(G125)snowpack conditions.The neural network model predictions are most consistent with the GPR derived estimates,Snowfork measurements,and snow pit data,achieving volumetric liquid water content deviation of≤1.5% and bulk density error within the range of 30-84 kg m^(-3).The results demonstrate that machine learning-based inversion,supported by realistic simulations and data augmentation enables scalable,non-invasive snowpack characterization with significant applications in hydrological forecasting,snow monitoring,and water resource management.展开更多
基金funded by the Natural Science Foundation of China(Grants No 42277127)。
文摘Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study investigates their coupled effects using inclinedflume experiments and Discrete Element Method(DEM)simulations,covering three gravel sizes(2-5 mm,5-7 mm,7-10 mm)and four ice-content levels(0%,20%,40%,60%).Run-out distance,velocity,energy components,flow regime(Savage number),and segregation indexαwere quantified.Increasing ice content significantly enhances mobility,but with diminishing marginal effectiveness.From 0%to 40%ice content,run-out distance increases by 41%-86%,whereas the additional increase from 40%to 60%contributes only 12%-23%.Particle-size ratio strongly governs segregation intensity.Fine-gravel groups reach segregation indices ofα=0.92-0.98,indicating nearly complete upward migration of ice,whereas medium-gravel and coarse-gravel groups exhibit much weaker segregation,stabilizing atα=0.68-0.74 and 0.60-0.69.Savage number analyses reveal marked flow-regime transitions.At 0%ice content,Savage numbers reach 1.0-1.5,indicating a collisional regime.Increasing ice content suppresses collisionality,with Savage numbers decreasing to 0.03-0.07 at 60%ice content,consistent with dense-regime flow.DEM energy analyses confirm this regime shift:for finegravel mixtures,collision energy decreases by 14%,while sliding-friction energy increases by 33%as ice content increases from 0%to 60%,reflecting enhanced overburden effects imposed by upward-segregated ice layers.Medium and coarse mixtures exhibit weaker or opposite energy-shift patterns,demonstrating strong size dependence.Mechanistically,large particle-size contrasts promote strong segregation and form dense basal rock layers that increase basal friction and reduce mobility.When particle sizes are similar or ice content is high,segregation remains limited,allowing ice to mix into the basal layer,thereby reducing basal friction and enhancing mobility.This research quantitatively demonstrates how composition controls particle spatial distribution,flow regime,and energy dissipation,offering new mechanistic insights into the propagation and deposition behaviors of rock-ice avalanches and improving hazard assessment in vulnerable high-mountain regions.
文摘准确评估雪层体积含水量和体积密度对于理解雪的水文过程、降低雪崩风险以及冰冻圈监测具有重要意义。本文提出一种新型双参数反演框架,该框架集成了合成电磁建模、降维方法和机器学习算法,用于从探地雷达(GPR)数据中提取相对介电常数和对数电阻率。传统雪层测量方法具有侵入性、劳动强度大且仅限于点位观测等局限性。为克服上述局限,建立了一种非侵入性、可扩展且数据驱动的框架,利用合成GPR数据集来表示具有不同含水量和密度分布的多样化雪层条件。使用先进电磁模拟器gprMax,通过有限差分时域模拟生成合成的一维时序反射(A扫描)。随后采用主成分分析(PCA)将每个A扫描进行压缩,得到低维且信息保真的特征集,从而显著提升模型训练效率。基于经过主成分分析(PCA)降维处理的特征集,训练了随机森林、神经网络、支持向量机和极限梯度提升四种机器学习模型。其中,神经网络模型性能最佳,介电常数为R^(2)>0.97,电阻率为R^(2)>0.92。合成数据中引入高斯噪声(信噪比约为6 dB),并通过针对特定领域进行改进,以提高其在实地数据的泛化能力。模型在中国阿尔泰山脉的两条典型GPR剖面(湿雪T750和饱和雪G125)上进行了验证。神经网络模型预测结果与GPR反演、Snowfork测量及人工雪坑数据高度一致,体积含水量偏差不超过1.5%,体积密度误差在30–84 kg m-3范围内。结果表明,基于机器学习的反演方法在结合真实模拟与数据增强的条件下,能够实现可扩展、非侵入式的雪层特性反演,在水文预报、雪情监测及气候敏感型水资源管理中具有重要应用价值。
文摘本文对2023年12月旅顺出现的连续10天强降雪天气事件进行回顾,运用多个站点观测资料、ERA5的位势高度场、海表温度场、中央气象台的天气形势图与近10年12月连续降雪事件加以对比总结,结果表明:2023年12月平均的冷涡强度最强(中心强度为5080位势米),强大的冷涡以及极为偏东的高空冷空气堆积是此次连续降雪事件的主要系统;渤海海峡的等温线分布有一个明显的向渤海以北延伸的暖舌(5.2℃~7.6℃之间),平均海气温差在9.4℃,这样冷空气在经过上游暖舌区域增温增湿后可给辽东半岛带来冷流降雪;此次连续降雪既有系统性降雪,又有冷流性降雪,其中冷流降雪(旅顺地区风速小),850 hpa或700 hpa分别处在涡后或槽后脊前,系统性降雪,500 hpa、700 hpa我区均处于高空槽前,850 hpa处于槽后脊前;冷流降雪前及过程中,辽东半岛北部中低空都出现了明显的垂直运动、水汽输送、水汽辐合,700 hpa相较850 hpa更为明显。In this paper, the 10 consecutive days of heavy snowfall in Lvshun in December 2023 was reviewed, and the observation data of several stations, the geopotential height field of ERA5, the sea surface temperature field, and the weather situation chart of the National Meteorological Center of CMA were compared with the continuous snowfall events in December in recent 10 years. The results show that: In December 2023, the average intensity of the cold vortex is the strongest (the central intensity is 5080 geopotential meters), and the strong cold vortex and the accumulation of high altitude cold air very far to the east are the main systems of this continuous snowfall event. The isothermal distribution of the strait has an obvious warm tongue (between 5.2˚C~7.6˚C) extending to the north of the Bohai Sea, the average temperature difference between the sea and the air is 9.4˚C, so that the cold air can bring cold flow snow to the Liaodong Peninsula after warming and humidification in the upstream warm tongue area. The continuous snowfall has both systematic snowfall and cold flow snowfall, among which cold flow snowfall (low wind speed in the Lvshun area), 850 hpa or 700 hpa are behind the vortex or in front of the rear ridge of the trough, and systematic snowfall, 500 hpa and 700 hpa are in front of the upper trough, and 850 hpa is in front of the rear ridge of the trough. Before and during the cold current snowfall, there are obvious vertical movements, water vapor transport and water vapor convergence in the middle and low levels in the northern part of the Liaodong Peninsula, the phenomenon is more pronounced at 700 hpa than 850 hpa.
基金supported by the National Key R&D Program of China(Grant Nos.2023YFC3008300&2023YFC3008305)the National Natural Science Foundation of China(Grant No.42172320)+1 种基金the Key Laboratory of Mountain Hazards and Engineering Resilience,Institute of Mountain Hazards and Environment,Chinese Academy of Sciences(Grant Nos.KLMHER-Z06&KLMHER-T07)the Science and Technology Research Program of Institute of Mountain Hazards and Environment,Chinese Academy of Sciences(Grant No.IMHE-CXTD.04).
文摘Accurate assessment of snowpack volumetric liquid water content and bulk density is essential for understanding snow hydrology,avalanche risk management,and monitoring cryosphere changes.This study presents a novel dual-parameter inversion framework that integrates synthetic electromagnetic modelling,dimensionality reduction,and machine learning algorithms to extract relative permittivity and log-resistivity from ground-penetrating radar(GPR)data.Traditional snowpack measurements are invasive,labor-intensive,and limited to point observations.To overcome these limitations,we developed a non-invasive,scalable,and data-driven framework that uses synthetic GPR datasets representing diverse snowpack conditions with variable moisture and density profiles.Synthetic 1D time series reflections(A-scans)are generated using finite-difference time-domain simulations in the state-of-the-art electromagnetic simulator gprMax.Principal component analysis(PCA)is applied to compress each A-scan while preserving key features,which significantly improved and enhanced the model training efficiency.Four machine learning models,including random forest,neural network,support vector machine,and eXtreme gradient boosting,are trained on PCA-reduced features.Among these,the neural network model achieved the best performance,with R^(2)>0.97 for permittivity and R 2>0.92 for resistivity.Gaussian noise(signal-to-noise ratio of 6 dB)is introduced to the synthetic data,and then targeted domain adaptation is employed to enhance generalization to field data.The framework is validated on two contrasting GPR transects in the Altay Mountains of the Chinese mainland,representing moist(T750)and wet(G125)snowpack conditions.The neural network model predictions are most consistent with the GPR derived estimates,Snowfork measurements,and snow pit data,achieving volumetric liquid water content deviation of≤1.5% and bulk density error within the range of 30-84 kg m^(-3).The results demonstrate that machine learning-based inversion,supported by realistic simulations and data augmentation enables scalable,non-invasive snowpack characterization with significant applications in hydrological forecasting,snow monitoring,and water resource management.
