极地冰雪氢氧同位素指标及其指示意义

本文综述了水汽来源状况、降水季节变化、火山喷发、太阳活动等气候环境因素对极地冰雪中同位素含量的可能影响及其程度 ,以及降水区域不均衡性、降水年际不均衡性、逆温层和同位素在冰雪中的扩散迁移作用等几种不稳定因素可能对冰雪同位素分析造成的干扰 ;在此基础上 ,综述了极地冰雪中δ1 8O、δD和其差值d(d =δD - 8δ1 8O)与局部或全球气温之间的一些线性关系 (包括全球尺度 ,格陵兰地区 ,南极地区等 )及它们在空间分布上的变化规律     

渭干河流域"2002·7"特大洪水分析

渭干河是塔里木河流域第六大源流, 位于天山西部南麓, 渭干河干流起点有新疆最大的流域性控制工程--克孜尔水库. 2002年7月下旬天山中西部山区出现大暴雨(雪)过程, 渭干河流域山区降水持续时间长达30 h以上, 山区降水量50 mm左右, 导致5条支流和渭干河干流出现有水文记录以来的最大洪峰, 流量超过警戒流量和危险流量的2～3.5倍, 暴雨(雪)过程结束之后, 融雪型洪峰长时间居高不下. 洪水过程中, 各支流以及暴雨与融雪等多种洪峰遭遇现象很明显. 克孜尔水库入库洪峰流量达3 660 m3*s-1, 经水库调洪错峰, 出库峰值流量为1 000 m3*s-1, 削峰率72.7%.     

气球携带碘化银焰弹技术研究

主要介绍在北京地区开展气球携带碘化银焰弹雨雪作业的作业技术，经济效益等问题。通过各种增雨雪技术比较，认敢球携带碘化银焰弹增雨雪作业在北方干旱地区的抗旱工作中或作为收音机增雨雪作业的补充手段具有应用价值     

L'archivage des activités humaines par les neiges et glaces polaires : le cas du plomb

The investigation of the occurrence of lead in dated snow and ice from Greenland and Antarctica has played a major role in our understanding of the history of the pollution of the atmosphere of our planet by this metal. Such studies have however proved to be very demanding, mainly because of the extreme purity of polar snow and ice. Reliable measurements can be obtained only if ultra-clean and highly sensitive procedures are used, as pioneered by Clair Patterson. The Greenland data show evidence of large-scale pollution of the atmosphere of the Northern Hemisphere for lead as early as two millennia ago during Greco–Roman times, especially because of mining and smelting activities in southern Spain. It peaked at the end of the 1960s, with lead concentrations in snow about 200 times higher than natural values, before declining during recent times because of the fall in the use of leaded gasoline. Lead pollution in Antarctica was already significant at the end of the 19th century as a consequence of whaling activities, the traffic of coal-powered ships crossing the Cape Horn, and mining activities in South America, South Africa and Australia. After declining because of the opening of the Panama Canal, the great economic depression and World War II, it reached a maximum during the 1980s, with lead concentrations 20 times higher than natural values. Other studies focus on past natural variations of lead in ancient ice dated from the last climatic cycles. To cite this article: C. Boutron et al., C. R. Geoscience 336 (2004).     

塔城市一次暴雪天气的分析

2003年2月22日塔城市出现一次暴雪天气过程。文章着重分析了此次天气过程的高空环流形势、地面形势、T—Td等，指出暖湿气流是造成暴雪的主要原因。     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

Diagnosing snow accumulation errors in a rain‐snow transitional environment with snow board observations

Diagnosing the source of errors in snow models requires intensive observations, a flexible model framework to test competing hypotheses, and a methodology to systematically test the dominant snow processes. We present a novel process‐based approach to diagnose model errors through an example that focuses on snow accumulation processes (precipitation partitioning, new snow density, and snow compaction). Twelve years of meteorological and snow board measurements were used to identify the main source of model error on each snow accumulation day. Results show that modeled values of new snow density were outside observational uncertainties in 52% of days available for evaluation, while precipitation partitioning and compaction were in error 45% and 16% of the time, respectively. Precipitation partitioning errors mattered more for total winter accumulation during the anomalously warm winter of 2014–2015, when a higher fraction of precipitation fell within the temperature range where partition methods had the largest error. These results demonstrate how isolating individual model processes can identify the primary source(s) of model error, which helps prioritize future research.     

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.     

Exploration of remotely sensed forest structure and ultrasonic range sensor metrics to improve empirical snow models

Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta‐analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables – obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper – and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR‐derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP‐derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically significant relationships between snow indicators and structural metrics by increasing mean r² by 20% when compared to manual surveys. The relationships between vegetation and spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size helped to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing technologies, and better represent spatial and temporal variations of snow. Copyright © 2013 John Wiley & Sons, Ltd.     

Rain on snow induced urban floods in cold maritime climate: Risk,indicators and trends

Urban floods pose a societal and economical risk. This study evaluated the risk and hydro-meteorological conditions that cause pluvial flooding in coastal cities in a cold climate. Twenty years of insurance claims data and up to 97 years of meteorological data were analysed for Reykjavík, Iceland (64.15°N; <100 m above sea level). One third of the city's wastewater collection system is combined, and pipe grades vary from 0.5% to 10%. Results highlight semi-intensive rain (<7 mm/h; ≤3 year return period) in conjunction with snow and frozen ground as the main cause for urban flood risk in a climate which undergoes frequent snow and frost cycles (avg. 13 and 19 per season, respectively). Floods in winter were more common, more severe and affected a greater number of neighbourhoods than during summer. High runoff volumes together with debris remobilized with high winds challenged the capacity of wastewater systems regardless of their age or type (combined vs. separate). The two key determinants for the number of insurance claims were antecedent frost depth and total precipitation volume per event. Two pluvial regimes were particularly problematic: long duration (13–25 h), late peaking rain on snow (RoS), where snowmelt enhanced the runoff intensity, elongated and connected independent rainfall into a singular, more voluminous (20–76 mm) event; shorter duration (7–9 h), more intensive precipitation that evolved from snow to rain. Closely timed RoS and cooling were believed to trigger frost formation. A positive trend was detected in the average seasonal snow depth and volume of rain and snowmelt during RoS events. More emphasis, therefore, needs to be placed on designing and operating urban drainage infrastructure with regard to RoS co-acting with frozen ground. Furthermore, more detailed, routine monitoring of snow and soil conditions is important to predict RoS flood events.