Incorporating accumulated temperature and algorithm of snow cover calculation into the snowmelt runoff model

An accurate simulation of snowmelt runoff is of much importance in arid alpine regions. Data availability is usually an obstacle to use energy‐based snowmelt models for the snowmelt runoff simulation, and temperature‐based snowmelt models are more appealing in these regions. The snow runoff model is very popular nowadays, especially in the data sparse regions, because only temperature, precipitation and snow cover data are required for inputs to the model. However, this model uses average temperature as index, which cannot reflect the snowmelt simulation in the high altitude band. In this study, the snow runoff model is modified on the basis of accumulated active temperature. Snow cover calculation algorithm is added and is no longer needed as input but output. This makes the model able to simulate long‐time runoff and long‐time snow cover variation in every band. An examination of the improved model in the Manas River basin showed that the model is effective. It can reproduce the behaviour of the hydrology and can reflect the actual snow cover fluctuation. Copyright © 2012 John Wiley & Sons, Ltd.     

Subgrid variability of snow water equivalent at operational snow stations in the western USA

The spatial distribution of snow water equivalent (SWE) is a key variable in many regional‐scale land surface models. Currently, the assimilation of point‐scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid‐scale SWE. To improve the understanding of the relationship between point‐scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1‐, 4‐ and 16‐km² grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger‐scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy. Copyright © 2012 John Wiley & Sons, Ltd.     

Response of snow processes to climate change: spatial variability in a small basin in the Spanish Pyrenees

In this study, the Cold Regions Hydrological Modelling platform was used to create an alpine snow model including wind redistribution of snow and energy balance snowmelt to simulate the snowpack over the period 1996–2009 in a small (33 ha) snow‐dominated basin in the Spanish Pyrenees. The basin was divided into three hydrological response units (HRUs), based on contrasting physiographic and aerodynamic characteristics. A sensitivity analysis was conducted to calculate the snow water equivalent regime for various combinations of temperature and precipitation that differed from observed conditions. The results show that there was large inter‐annual variability in the snowpack in this region of the Pyrenees because of its marked sensitivity to climatic conditions. Although the basin is small and quite homogeneous, snowpack seasonality and inter‐annual evolution of the snowpack varied in each HRU. Snow accumulation change in relation to temperature change was approximately 20% for every 1 °C, and the duration of the snowpack was reduced by 20–30 days per °C. Melting rates decreased with increased temperature, and wind redistribution of snow was higher with decreased temperature. The magnitude and sign of changes in precipitation may markedly affect the response of the snowpack to changes in temperature. There was a non‐linear response of snow to individual and combined changes in temperature and precipitation, with respect to both the magnitude and sign of the change. This was a consequence of the complex interactions among climate, topography and blowing snow in the study basin. Copyright © 2012 John Wiley & Sons, Ltd.     

A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products

ABSTRACT

A model fusion approach was developed based on five artificial neural networks (ANNs) and MODIS products. Static and dynamic ANNs – the multi-layer perceptron (MLP) with one and two hidden layers, general regression neural network (GRNN), radial basis function (RBF) and nonlinear autoregressive network with exogenous inputs (NARX) – were used to predict the monthly reservoir inflow in Mollasadra Dam, Fars Province, Iran. Leaf area index and snow cover from MODIS, and rainfall and runoff data were used to identify eight different combinations to train the models. Statistical error indices and the Borda count method were used to verify and rank the identified combinations. The best results for individual ANNs were combined with MODIS products in a fusion model. The results show that using MODIS products increased the accuracy of predictions, with the MLP with two hidden layers giving the best performance. Also, the fusion model was found to be superior to the best individual ANNs.     

中国西部不同类型冰川区积雪及其融水径流中稳定同位素比率的时空变化及其气候效应

为了调查和比较我国海洋型、大陆型和极大陆型冰川覆盖区大气降水、冰川、积雪和融水径流系统内稳定同位素比率的时空分布特征和冰雪相变时的现代同位素分馏过程,在2000—2003年间,按照季节和海拔高度分别对玉龙雪山、慕士塔格峰,念青唐古拉山的桑丹康桑峰以及天山乌鲁木齐河源的1号冰川积雪和冰雪融水径流进行了系统的采样研究.结果表明:在以玉龙雪山为代表的海洋型冰川区,新雪内的1δ8O呈现出显著的时间与空间变化,在冬季表现为“高度效应”,即δ18O值随海拔高度升高和气温降低而降低,反映出冬季风降水的特征;而经过融化的夏季积雪受到“降水量效应”、雪中含水量、蒸发等因素的影响,稳定同位素产生分馏变化“高度效应”空间变化比较复杂,反映出夏季风降水的特征,念青唐古拉山夏季积雪也有相似的特征.在极大陆型冰川分布的慕士塔格山地区和亚大陆型冰川分布的天山乌鲁木齐河源地区,夏季新雪中1δ8O则表现为“高度效应”或者“温度效应”.无论在海洋型冰川或者大陆型冰川分布区,经过融化或正在消融的残余积雪内都有明显的同位素分馏变化,1δ8O值比新雪高.受蒸发作用的影响,冰雪融水在流动过程中,1δ8O随海拔高度的降低、流程和流动时间的增加而增加.由于气候条件的差异,海洋型冰川区冰雪融水相变过程中的同位素分馏和化学作用都比大陆型冰川区强.     

世界海拔8000m以上雪山研究

实地科考,广博查询,长期探索,坚韧追求,人类首次、全面解开世界海拔8 000m以上雪山之迷:海拔8 000m级雪山地有17处(其中3处山地为新发现),面积共11.95414km2,海拔8 000m以上独立山峰39座.世界海拔8 000m级山地和山峰地貌形态、相互关系,己在即将出版的"世界八千米雪山地图集"中得到表示.该图集比例尺1∶100000,等高距40m,幅面550mm×790mm.该图集由序言、7幅地图和后记组成,是世界第一部关于海拔8 000m级雪山地图集.     

冰川表层雪样中微生物总DNA几种提取方法的比较研究

通过对4种具有不同裂解方式的微生物总DNA提取方法的比较,筛选出较佳的方法进行滤膜处理、DNA抽提及沉淀的优化,再将优化出的传统DNA提取方法与商业化试剂盒提取方法比较.结果表明:商业化试剂盒提取总DNA的方法适用于冰川表层雪微生物总DNA的提取;其次,将滤膜剪碎,用溶菌酶(5mg.mL-1)+蛋白酶K(1mg.mL-1)+CTAB(1%)+SDS(1%)的裂解方式,氯仿:异戊醇(24∶1)抽提一次,乙醇沉淀DNA的提取方法是一种效果较佳且廉价的冰川表层雪微生物总DNA提取方法.     

新疆北疆地区季节性冻土结冻过程与日积温的关系

传统的度日因子模型很难分辨在结冻期土壤每日结冻和解冻的过程,而日小时积温可以区分正积温和负积温对土壤冻结过程的影响.利用北疆地区1951-2010年气象站数据和决策树算法,分析计算日小时积温及表层5cm和10cm土壤冻结状态数据及日小时积温对季节性冻土冻结现象的影响.结果表明:在北疆范围年小时正积温以每年平均160℃增长,而年小时负积温以每年平均153℃减少.季节性冻土发生冻结现象所需的临界值分布与北疆地区气候和土壤分布基本一致,但仍存在空间差异性.北疆地区5cm土壤结冻所需的日小时负积温为-50℃以下,而5cm到10cm土壤结冻所需日小时负积温的平均值差值为-15℃左右.与日最低气温和日平均气温作为土壤结冻判据相比,日小时积温临界值作为判据可获得较高的精确度.在昌吉地区和阿勒泰地区冻土的平均深度随着日小时负积温临界值的增加而减少.     

中国近50a积雪日数与最大积雪深度的时空变化规律

通过REOF和非参数Mann-Kendall趋势检验法,以1958/1959-2007/2008年度中国557个气象台站的积雪观测资料为基础,对中国积雪日数与最大积雪深度的时空演变规律进行分析.结果表明:东北、新疆北部和青藏高原中东部为中国积雪日数和最大积雪深度的3个大值区;近50a来,春、秋季中国积雪日数和最大积雪深度在整体上呈现缓慢减少的趋势,冬季积雪日数和最大积雪深度呈现增加的趋势.气温是影响积雪产生和维持的重要因素.     

基于GRACE数据的长江流域地表水储量变化

基于GRACE RL06 Level-2卫星产品数据,采用高斯滤波、P3M9去相关滤波等方法对2002年4月-2017年6月长江流域地表水储量变化进行了反演。结果表明：近16年来,长江流域整体水储量处在一个缓慢上升的状态,地表水储量变化速率为4.1±1.1 mm/yr,总体水量在这一期间增加了大约1.1×1011T,基于水文要素的统计分析显示这一结果较为准确的反映了长江流域的水量收支状态。