Avalanche activity and characteristics of its triggering factors in the western Tianshan Mountains,China

Snow avalanche is a serious threat to the safety of roads in alpine mountains. In the western Tianshan Mountains, large scale avalanches occur every year and affect road safety. There is an urgent need to identify the characteristics of triggering factors for avalanche activity in this region to improve road safety and the management of natural hazards. Based on the observation of avalanche activity along the national road G218 in the western Tianshan Mountains, avalanche event data in combination with meteorological, snowpack and earthquake data were collected and analyzed. The snow climate of the mountain range was examined using a recently developed snow climate classification scheme, and triggering conditions of snow avalanche in different snow climate regions were compared. The results show that snowfall is the most common triggering factor for a natural avalanche and there is high probability of avalanche release with snowfall exceeding 20.4 mm during a snowfall period. Consecutive rise in temperature within three days and daily mean temperature reaching 0.5℃ in the following day imply a high probability of temperaturerise-triggered avalanche release. Earthquakes have a significant impact on the formation of large size avalanches in the area. For the period 2011-2017, five cases were identified as a consequence of earthquake with magnitudes of 3.3≤M_L≤5.1 and source-to-site distances of 19~139 km. The Tianshan Mountains are characterized by a continental snow climate with lower snow density, lower snow shear strength and high proportion depth hoar, which explains that both the snowfall and temperature for triggering avalanche release in the continental snow climate of the Tianshan Mountains are lower than that in maritime snow climate and transitional snow climate regions. The findings help forecast avalanche release for mitigating avalanche disaster and assessing the risk of avalanche disaster.

Snow cover estimation from MODIS and Sentinel-1 SAR data using machine learning algorithms in the western part of the Tianshan Mountains

Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component analysis–support vector machine(PCA–SVM) method is proposed for snow cover mapping through the integration of moderateresolution imaging spectroradiometer(MODIS) snow cover products and the Sentinel-1 synthetic aperture radar(SAR) scattering characteristics. First, derived from the Sentinel-1 A SAR images, the feature parameters, including VV/VH backscatter, scattering entropy, and scattering alpha, were used to describe the variations of snow and non-snow covers. Second, the optimum feature combinations of snow cover were formed from the feature parameters using the principle component analysis(PCA) algorithm. Finally, using the optimum feature combinations, a snow cover map with a 20 m spatial resolution was extracted by means of an object-based SVM classifier. This method was applied in the study area of the Xinyuan County, which is located in the western part of the Tianshan Mountains in Xinjiang, China. The accuracies in this method were analyzed according to the data observed at different experimental sites. Results showed that the snow cover pixels of the extraction were less than those in the actual situation(FB1=93.86, FB2=59.78). The evaluation of the threat score(TS), probability of detection(POD), and false alarm ratio(FAR) for the snow-covered pixels obtained from the two-stage SAR images were different(TS1=86.84, POD1=90.10, FAR1=4.01;TS2=56.40, POD2=57.62, FAR2=3.62). False and misclassifications of the snow cover and non-snow cover pixels were found. Although the classifications were not highly accurate, the approach showed potential for integrating different sources to retrieve the spatial distribution of snow covers during a stable period.

Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China

Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.

Existing Condition Analysis of Dry Spent Fuel Storage Technology

As in some domestic nuclear power plants,spent fuel pools near capacity,away-from-reactor type storage should be arranged to transfer spent fuel before the pool capacity is full and the plants can operate in safety.This study compares the features of wet and dry storage technology,analyzes the actualities of foreign dry storage facilities and then introduces structural characteristics of some foreign dry storage cask.Finally,a glance will be cast on the failure of away-from-reactor storage facilities of Pressurized Water Reacto(rPWR)to meet the need of spent-fuel storage.Therefore,this study believes dry storage will be a feasible solution to the problem.

Numerical simulation of seasonal snow in Tianshan Mountains

Snowfall in the Tianshan Mountains in China is frequent during winter;thus,avalanches have become a severe issue in snow-covered areas.Accumulation and metamorphosis,as well as hydrothermal exchanges with the environment,considerably affect the stability of snow on slopes.Therefore,a hydrothermal model of snow cover and its underlying surfaces must be developed on the basis of meteorological data to predict and help manage avalanches.This study adopted the conceptual model of snow as a porous medium and quantitatively analysed its internal physical processes on the basis of the thermal exchanges amongst its components.The effects of local meteorological factors on snow structure and the redistribution of energy and mass inside the snow cover in the Tianshan Mountains were simulated.Simulation results showed that deformation as a result of overlying snow and sublimation of snow cover at the bottom is the main cause of density variation in the vertical profile of snow cover.Temperature drives water movement in snow.The low-density area of the bottom snow is the result of temperature gradient.The simulation results of the long-term snow internal mass distribution obtained by the method established in this study are highly consistent with the actual observed trend of variation.Such consistency indicates an accurate simulation of the physical characteristics of snow cover in small and microscale metamorphism in the Tianshan Mountains during the stable period.

巩乃斯河谷地带地表土壤水分和电导率的分布特征

土壤水分和土壤电导率是影响植被生长和农业发展的重要因素,传统方法测量土壤水分和土壤电导率成本高,导致当前缺少基于实测资料的这两种土壤变量在山区流域不同空间尺度下的研究。本研究利用地表土壤水分和电导率实测数据,应用统计学方法,分析了天山西部巩乃斯河谷地带不同空间尺度下秋季地表土壤水分和地表土壤电导率的分布特征及其影响因素。结果表明:(1)在巩乃斯河谷,流域尺度地表土壤水分随海拔的升高而显著(P<0.01)增加,增加速率为每公里10%,但在阿拉图拜小流域,地表土壤水分随海拔的升高先上升后降低;流域尺度平均地表土壤电导率为17.51 mS·m^(-1),表层土壤整体上不存在盐碱化,但2000~2500 m海拔范围内部分点的电导率大于35 mS·m^(-1),为轻度盐渍化;坡地尺度下,不同坡向地表土壤水分和电导率均存在较大差异,阴坡地表土壤水分(均值:44.22%)>阳坡(均值:22.83%),阳坡地表土壤电导率(均值:8.33 mS·m^(-1))>阴坡(均值:4.58 mS·m^(-1));(2)随空间尺度增大,影响土壤特性的环境要素更加复杂,地表土壤水分与地表土壤电导率关系逐渐变弱;(3)不同土地利用类型的地表土壤水分依次为:草地(均值37.19%)>耕地(均值37.04%)>林地(均值34.67%),地表土壤电导率依次为:耕地(均值17.36 mS·m^(-1))>草地(均值14.95 mS·m^(-1))>林地(均值13.81 mS·m^(-1))。在海拔、坡向、土地利用类型的影响下,巩乃斯河谷地带地表土壤电导率和土壤水分均呈中度变异。本研究有益于巩乃斯河流域水土资源合理利用及生态环境保护。