Local and regional variability in snow conditions in northern Finland: A reindeer herding perspective

Weather station measurements were used to force the SNOWPACK snow model and combined with reindeer herders’ experiences to study the local and regional variations in snow conditions in a Finnish reindeer herding area for the 1981–2010 period. Winter conditions varied significantly between the four selected herding districts and between open and forest environments within the districts. The highest snow depths and densities, the thicknesses of ground ice, and the lengths of snow cover period were generally found in the northernmost districts. The snow depths showed the strongest regional coherence, whereas the thicknesses of ground ice were weakly correlated among the districts. The local variation in snow depths was higher than the regional variation and limits for rare or exceptional events varied notably between different districts and environments. The results highlight that forests diversify snow and foraging conditions, e.g., ground ice rarely forms simultaneously in different environments. Sufficient and diverse forest pastures are important during the critical winter season if reindeer herding is pursued on natural grazing grounds also in the future.     

Testing and Improving Temperature Thresholds for Snow and Rain Prediction in the Western United States

The phase of precipitation at the land surface is critical to determine the timing and amount of water available for hydrological and ecological systems. However, there are few techniques to directly observe the precipitation phase and many prediction tools apply a single temperature threshold (e.g., 0°C) to determine phase. In this paper, we asked two questions: (1) what is the accuracy of default and station optimized daily temperature thresholds for predicting precipitation phase and (2) what are the regions and conditions in which typical temperature‐based precipitation phase predictions are most suited. We developed a ground truth dataset of rain vs. snow using an expert decision‐making system based on precipitation, snow depth, and snow water equivalent observations. This dataset was used to evaluate the accuracy of three temperature‐threshold‐based techniques of phase classification. Optimizing the temperature threshold improved the prediction of precipitation phase by 34% compared to using 0°C threshold. Developing a temperature threshold based on station elevation improved the error by 12% compared with using the 0°C temperature threshold. We also found the probability of snow as a function of temperature differed among ecoregions, which suggests a varied response to future climate change. These results highlight a current weakness in our ability to predict the effects of regional warming that could have uneven impacts on water and ecological resources.     

Defining the Diurnal Pattern of Snowmelt Using a Beta Distribution Function

Snow is an important component of the hydrologic cycle for many regions worldwide. In addition to vital water resources, snowmelt can be important for forest ecosystem dynamics and flood risk. However, standard design events in the United States lack a design snowmelt event, including only precipitation events, though snowmelt has been shown to be larger than rainfall. In this article, we present a method using hourly snow water equivalent data to develop and test a function for representing the diurnal pattern of snowmelt. A two‐parameter beta distribution function is modified for the purposes of this study and found to fit the pattern of snowmelt well with a root mean squared error of 0.008. Soil moisture sensors were additionally utilized to assess the timing of the snowmelt water outflow from the base of the snowpack that supports the shape of the function, but suggests that the timing of losses recorded on snow pillows lag as much as 3 h. Further testing of the function showed the shape of the function to be accurate. The methods developed and tested in this paper can be applied for design purposes comparing snowmelt and rainfall events or to improve hydrological models investigating processes such as streamflow or groundwater recharge.     

南京近50年冬季低温冰冻积雪事件的气候特征

应用南京逐日观测资料分析了南京低温、冰冻和积雪灾害的变化特征及其与区域气候变暖的联系,得到如下结论:①低温、冰冻、积雪事件都存在一定的年代际和年际的周期变化;冬季平均最低气温和极端最低气温具有相似的周期性特征;积雪深度的年代际尺度的周期特征明显,但是较小尺度的年际周期不明显;②近50年来南京的低温、冰冻、积雪事件呈现波动下降的趋势;③南京年均气温和低温、冰冻、积雪事件之间存在较强的相关性与区域气候变暖关系密切。     

重大冰雪灾害应急管理能力的评价——以湖南省为例

针对重大冰雪灾害应急管理的复杂性,基于湖南冰雪灾害案例以及国内外自然灾害及其应急管理的相关文献,提出了重大冰雪灾害应急管理能力评价指标结构。运用熵权法和群决策方法确定指标综合权重,采用群决策方法获得专家群体对一级评价指标的综合偏好,形成评价城市的综合评价矩阵,运用灰色综合评价模型求出各个评价城市应急管理能力的排序向量。最后以湖南省冰雪灾害为案例进行了应用。     

季节性雪被对青藏高原东缘高寒草甸土壤氮矿化的影响

依据自然雪被分布的差异,在青藏高原东缘高寒草甸中设置3条样带(即深雪、中等厚度雪被和浅雪),于2008年的秋冬过渡期,连续监测各样带中的雪被厚度和土壤温度,并采用原位培养法测定每月的土壤氮素氨化、硝化和矿化速率,以研究不同厚度雪被对高寒草甸土壤氮矿化的影响.结果表明,月均土温、每月日最高土温均值分别与雪被厚度极显著相关,二次函数关系拟合较好(R2=0.576,0.685),且根据每月日最高土温均值与雪被厚度的二次函数关系方程可知,25 cm厚的雪被可以起到较好的隔绝效果;土壤含水量受雪被厚度和土壤温差两个因素的显著影响.在秋冬过渡期末,浅雪梯度下土壤硝态氮含量显著降低,且雪被下的净氮矿化速率与月均土温、每月日最高土温均值、每月日最低土温均值都分别呈极显著相关,二次函数关系拟合较好(R2=0.589,0.541,0.601).研究表明,不同厚度的雪被对土壤温度和含水量影响显著,从而显著地影响着土壤氮的矿化,深雪更有利于氨化、硝化和氮矿化.图7表2参36     

极端冰雪条件下的顺层岩质边坡滑移稳定性分析

推导了典型岩石边坡在极端冰雪条件下的滑移稳定系数的表达式;通过计算分析,揭示了岩石边坡滑移稳定系数随裂隙内水深、坡高、坡角、滑面倾角等因素变化的规律及与冻深的关系,并绘制岩石边坡滑移稳定系数与边坡几何要素之间的关系图.研究表明,当考虑极端冰雪灾害影响时,岩石边坡滑移稳定系数发生较为明显的变化:随裂隙饱水程度、坡面角、主...     

陕南地区清代霜雪灾害及分形特征研究

通过对陕南清代历史文献资料的搜集、整理和分析,研究了清代(1644-1911年)陕南霜雪灾害等级、阶段变化及不同等级灾害周期性等。统计分析显示,陕南清代发生霜雪灾害28次,平均每9.57年发生一次;霜雪灾害可划分为轻度、中度、重度三级,各占灾害总频次的21%,72%和7%;清代霜雪灾害可分为4个阶段,其中1644-1668年和1819-1868年的第1、第3阶段为灾害多发期,而1669-1818年和1869-1911年的第2、第4阶段为灾害少发期。霜雪灾害的自相似性揭示了灾害的分形性,分形结果显示陕南地区清代各等级灾害呈周期变化,且这些灾害的集中性非常强。陕南清代轻度、中度和重度霜雪灾害发生分别存在着16～18 a,7～8 a和46 a振荡周期。该地区霜雪灾害的发生主要是偏暖月的持续性降雪、积雪或由寒流引起的气温骤降造成的。初步确定陕南清代发生了两次霜雪灾害气候事件,时间在公元1649-1663年和1817-1842年。     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

基于SARIMA模型的黑龙江省冰雪旅游国际需求预测

选取黑龙江省1990-2011年冰雪旅游入境人次的月度时间序列为研究样本，建立SARIMA模型对黑龙江省冰雪旅游国际需求进行分析预测。结果表明，SARIMA（4，1，4）（1，1，1）。模型的拟合度和短期预测效果都较好。通过应用SARIMA模型进行冰雪旅游预测，以期对黑龙江省旅游企业的冰雪旅游国际客源市场开发及政府部门制定冰雪旅游发展战略和政策调整提供重要的理论依据。