Variations in glacier volume and snow cover and their impact on lake storage in the Paiku Co Basin,in the Central Himalayas

Glaciers and snow cover are important constituents of the surface of the Tibetan Plateau. The responses of these phenomena to global environmental changes are sensitive, rapid and intensive due to the high altitudes and arid cold climate of the Tibetan Plateau. Based on multisource remote sensing data, including Landsat images, MOD10A2 snow product, ICESat, Cryosat-2 altimetry data and long-term ground climate observations, we analysed the dynamic changes of glaciers, snow melting and lake in the Paiku Co basin using extraction methods for glaciers and lake, the degree-day model and the ice and lake volume method. The interaction among the climate, ice-snow and the hydrological elements in Paiku Co is revealed. From 2000 to 2018, the basin tended to be drier, and rainfall decreased at a rate of −3.07 mm/a. The seasonal temperature difference in the basin increased, the maximum temperature increased at a rate of 0.02°C/a and the minimum temperature decreased at a rate of −0.06°C/a, which accelerated the melting from glaciers and snow at rates of 0.55 × 10⁷ m³/a and 0.29 × 10⁷ m³/a, respectively. The rate of contribution to the lake from rainfall, snow and glacier melted water was 55.6, 27.7 and 16.7%, respectively. In the past 18 years, the warmer and drier climate has caused the lake to shrink. The water level of the lake continued to decline at a rate of −0.02 m/a, and the lake water volume decreased by 4.85 × 10⁸ m³ at a rate of −0.27 × 10⁸ m³/a from 2000 to 2018. This evaluation is important for understanding how the snow and ice melting in the central Himalayas affect the regional water cycle.     

Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.     

Talus slope geomorphology investigated at multiple time scales from high-resolution topographic surveys and historical aerial photographs (Sanetsch Pass,Switzerland)

Talus slopes are common places for debris storage in high-mountain environments and form an important step in the alpine sediment cascade. To understand slope instabilities and sediment transfers, detailed investigations of talus slope geomorphology are needed. Therefore, this study presents a detailed analysis of a talus slope on Col du Sanetsch (Swiss Alps), which is investigated at multiple time scales using high-resolution topographic (HRT) surveys and historical aerial photographs. HRT surveys were collected during three consecutive summers (2017–2019), using uncrewed aerial vehicle (UAV) and terrestrial laser scanning (TLS) measurements. To date, very few studies exist that use HRT methods on talus slopes, especially to the extent of our study area (2 km²). Data acquisition from ground control and in situ field observations is challenging on a talus slope due to the steep terrain (30–37°) and high surface roughness. This results in a poor spatial distribution of ground control points (GCPs), causing unwanted deformation of up to 2 m in the gathered UAV-derived HRT data. The co-alignment of UAV imagery from different survey dates improved this deformation significantly, as validated by the TLS data. Sediment transfer is dominated by small-scale but widespread snow push processes. Pre-existing debris flow channels are prone to erosion and redeposition of material within the channel. A debris flow event of high magnitude occurred in the summer of 2019, as a result of several convective thunderstorms. While low-magnitude (<5,000 m³) debris flow events are frequent throughout the historical record with a return period of 10–20 years, this 2019 event exceeded all historical debris flow events since 1946 in both extent and volume. Future climate predictions show an increase of such intense precipitation events in the region, potentially altering the frequency of debris flows in the study area and changing the dominant geomorphic process which are active on such talus slopes. © 2020 John Wiley & Sons, Ltd.     

新疆伊犁地区季节冻土沿海拔的分布规律及其影响因素

基于2000 - 2014年新疆伊犁地区不同海拔区域观测的冻融期内的冻土、 积雪和气象数据， 应用相关性分析和回归分析方法， 分析该地区季节冻土沿海拔的分布规律， 以及气温、 积雪对季节冻土特征的影响。结果表明： 伊犁地区表层土壤存在着每年11月份开始结冻， 于次年4月份完全融化的周期性变化。每个周期内土壤冻结时长随海拔以4 d·（100m）^-1的趋势增加， 土壤最大冻结深度随海拔以3.9 cm·（100m）^-1的趋势增加。土壤冻结时长与冻结期的平均气温具有显著负相关关系， 相关系数为-0.98（P<0.05）。土壤冻结日数与积雪覆盖历时呈正相关关系， 土壤的最大冻结深度与最大雪深呈负相关关系。随着海拔升高， 温度递减， 导致伊犁地区土壤最大冻结深度和土壤冻结日数整体呈现增加趋势。但在海拔相对较高的地区， 由于相对较厚积雪的影响， 出现土壤最大冻结深度随海拔升高而减小的反常现象。研究结果可为新疆伊犁地区季节冻土的分布对气候变化的响应研究提供支持， 帮助研究区域生态规划和水资源管理， 为农业发展制定适应气候变化对策。     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

A rain on snow climatology and temporal analysis for the eastern United States

ABSTRACT

Rain-on-snow (ROS) has the potential to produce devastating floods by enhancing runoff from snowmelt. Although a common phenomenon across the eastern United States, little research has focused on ROS in this region. This study used a gridded observational snow dataset from 1960–2009 to establish a comprehensive seasonal climatology of ROS for this region. Additionally, different rain and snow thresholds were compared while considering temporal trends in ROS occurrence at four grid cells representing individual locations. Results show most ROS events occur in MAM (March-April-May). ROS events identified with rainfall >1 cm are more frequent near the east coast and events identified with >1 cm snow loss are more common in higher latitudes and/or elevations. Decreasing trends in DJF (December-January-February) ROS events were identified near the coastal areas, with increasing trends in the northern portion of the domain. Significant decreasing trends in MAM ROS are likewise present on a regional scale. Factors playing a role in snowpack depth and rainfall, such as movement of storm tracks in this region, should be considered with future work to discern mechanisms causing the changes in ROS frequency.     

基于MODIS积雪产品的高亚洲融雪末期雪线高度遥感监测

以2001—2016年逐日MODIS积雪产品为主要数据源,在高亚洲区域发展了大尺度融雪末期雪线高度的遥感提取方法,并对其2001—2016年的时空变化特征进行了分析。提取方法首先对逐日的MODIS积雪覆盖率产品进行去云处理,获得积雪覆盖日数（SCD）数据集;并用冰川年物质平衡观测数据、融雪末期Landsat数据对提取终年积雪的MODIS SCD阈值进行率定;最后以MODIS SCD提取的终年积雪面积结合地形“面积—高程”曲线实现大尺度融雪末期雪线高度信息的提取。结果表明：① 高亚洲融雪末期雪线高度的空间异质性较强,总体上呈南高北低的纬度地带性分布规律;并因受山体效应的影响,雪线高度由高海拔地区向四周呈环形逐渐降低的特点。② 高亚洲2001—2016年融雪末期雪线高度总体上表现为明显的增加趋势。在744个30 km的监测格网中,24.2%的格网雪线高度呈显著增加,而仅0.9%的格网呈显著下降。除兴都库什、西喜马拉雅外,其他地区雪线高度均表现为升高趋势,显著上升的地区主要分布在天山、喜马拉雅中东部和念青唐古拉山等,其中以东喜马拉雅升高最为显著（8.52 m yr ^-1）。③ 夏季气温是影响高亚洲融雪末期雪线高度变化的主要因素,两者具有显著的正相关关系（R = 0.64,P < 0.01）。     

基于GPS新型L5信号的地表雪深反演研究

利用GPS多路径反射信号测量地表雪深具有全天候和高时空分辨率的特点，因此其可作为一种代替气象站监测雪深的新手段。然而，先前大多数研究仅使用了GPS L1和L2C波段信噪比数据探测积雪深度。为验证新型的L5信号在雪深反演方面的优越性，本文阐述了GPS-R技术反演雪深的原理，利用Lomb-Scargle周期图法所处理的受积雪表层影响的信噪比数据计算了频谱振幅强度，通过获取频谱特征值与天线高度的关系求解雪深值，最后分别与L1反演结果和实测雪深数据进行了对比。试验结果表明：与现有的GPS-R测量雪深结果相比，利用新型的L5反射信号反演地表雪深的精度更佳；采用GPS-R技术探测雪深对把握测站区域内的雪深变化情况和淡水资源储量具有重要价值。     

A new method for establishing hydrologic fidelity of snow depth measurements based on snowmelt–runoff hydrographs

For snowmelt-driven flood studies, snow water equivalent (SWE) is frequently estimated using snow depth data. Accurate measurements of snow depth are important in providing data for continuous hydrologic simulations of such watersheds. A new hydrologic fidelity metric is proposed in this study to evaluate the potential contribution of particular snow depth datasets to flow characteristics using observed data and hydrologic modeling using the Variable Infiltration Capacity (VIC) model. Data-based hydrologic fidelity of snow depth measurements is defined as a categorical skill score between the snow depth in the watershed and the hydrograph peak or volume at the watershed outlet. Similarly, model-based hydrologic fidelity is defined as a categorical skill score between the model-simulated snow depth and the model-simulated hydrograph peak or volume. The proposed framework is illustrated using the Pecatonica River watershed in the USA, indicating which sites have a higher hydrologic fidelity, which is preferred in hydrologic studies.