川藏交通廊道伯舒拉岭隧道出口斜坡雪崩活动特征及形成机理

伯舒拉岭隧道出口是川藏交通廊道全线在雪崩灾害背景下选址挑战最大的一处工点,开展雪崩活动特征及其形成机理研究对于川藏交通廊道减灾选线以及后续建设、运营都有重大的科学意义.通过精细化野外调查、长时序多源遥感解译、地形计算和空间分析,精细刻画伯舒拉岭隧道出口斜坡雪崩范围的地形地貌特征和历史活动特征,结合孕灾条件、逐日气象数据等,深入研究该斜坡雪崩成因、启动机制及对铁路的影响.结果显示,川藏交通廊道伯舒拉岭隧道出口斜坡发育4处沟槽型雪崩;隧道出口北侧1#雪崩形成区汇雪面积最大,约0.726 km2;4处雪崩的历年活动频率不一,主要发生在每年1月至5月,其中汇雪面积最大的1#雪崩活动最频繁,每年发生2～5次,其崩出距离亦最远,最大崩出距离约1 694 m;雪崩活动与年初几次区域性的高强度降雪活动高度相关,一般发生在高强度降雪期间和高强度降雪后几日的晴天.结果表明地形地貌条件控制了雪崩的空间分布,气象要素的时序变化控制了雪崩的年内尺度时序分布;4处雪崩形成区的面积大小和坡向分异决定了它们活动频率的不一致;雪崩启动模式主要有强降雪启动和气温陡升启动;隧道出口受4处雪崩威胁的可能性较小,风险可控.     

藏东南帕隆藏布流域雪崩关键影响因素与易发性区划研究

雪崩灾害是青藏高原广泛分布的一类灾害,通过对雪崩的关键影响因素分析,构建雪崩灾害易发性评价体系,可为布局在青藏高原的川藏铁路等重大工程建设的防灾减灾工作提供科学支撑。本文以藏东南帕隆藏布流域为例,基于遥感解译和野外调查,识别出381个崩至林线以下的沟槽型雪崩范围,综合选取了18个雪崩影响因子,运用主成分分析法(PCA)对影响因子进行分析,获得了帕隆藏布流域雪崩发育的关键影响因素,并赋予各影响因素权重,通过加权信息量(PCA-Ⅰ)和加权确定性系数(PCA-CF)进行雪崩易发性区划,采用ROC曲线进行精度检验。结果表明,帕隆藏布流域内雪崩活动的关键影响因素可归纳为气候气象、宏观地形、微观地形和抑制作用成分4类主成分因素,其中气候气象解释了30.61%的数据变异,地形地貌解释了21.23%的数据变异;PCA-Ⅰ模型计算的雪崩易发性区划指数在[-2.41,1.365]区间内,PCA-CF得到雪崩易发性区划指数在[-0.549,0.424]区间内,两者ROC曲线的AUC均大于0.70;但PCA-Ⅰ模型计算的雪崩易发性结果在帕隆藏布下游通麦段的河谷区呈现明显的异常区,相对而言,PCA-CF模型计算的雪崩易发性区划指数更合理,且其ROC曲线的AUC评价精度高达0.913。整体结果表明雪崩高易发区域主要分布于帕隆藏布上游窄谷段(然乌至玉普段)、中下游(玉普至通麦段)两岸山岭的山脊部位和各支流窄谷段。     

Snow avalanche susceptibility evaluation in the central Shaluli Mountains of Tibetan Plateau based on machine learning method北大核心CSCD

Snow avalanches,which are widely and frequently developed at high elevations,seriously threatens the built traffic corridors in the Tibetan Plateau. Susceptibility evaluation of snow avalanche via machine learning model with a high forecast accuracy can be appled to quickly and effectively assess the regional avalanche risk. This paper took the central Shaluli Mountain region as the study area,in which the snow avalanche inventory was established through remote sensing interpretation and field investigation verification. We quantitatively extracted 17 evaluation factors via GIS-based analysis,and these factors were selected through the variance expansion factor（VIF）. Four machine learning models containing SVM,DT,MLP and KNN were used to compile the susceptibility index map of snow avalanches,and kappa coefficient and ROC curve were used to verify the accuracy. The results suggested that the susceptibility indexes obtained from SVM,DT,MLP and KNN were in the range of［0,0. 964］,［0,815］,［0,0. 995］and［0,1］,respectively. The accuracy test results show that these four models all have good prediction accuracy. Among them,the SVM model is the best. The results also indicated that the areas with the high snow avalanche susceptibility mainly distributed in Genie Mountain and Rigong Mountain,most of which were above the planation surface of the Tibetan Plateau. The average altitude of the extremely high snow-avalanche-prone areas is 4 939 m,while the average altitude of the high snow avalanche-prone areas is 4 859 m. The snow avalanche has low perniciousness on the Sichuan-Tibet Highway and the Sichuan-Tibet Railway in the study area. This study can provide theoretical basis and method reference for disaster prevention and mitigation of snow avalanche along Sichuan-Tibet Railway and other major projects across Shaluli Mountains region. © 2022 Science Press (China).