Response of snow hydrological processes to a changing climate during 1961 to 2016 in the headwater of Irtysh River Basin, Chinese Altai Mountains

With changing climatic conditions and snow cover regime, regional hydrological cycle for a snowy basin will change and further available surface water resources will be redistributed. Assessing snow meltwater effect on runoff is the key to water safety, under climate warming and fast social-economic developing status. In this study, stable isotopic technology was utilized to analyze the snow meltwater effect on regional hydrological processes, and to declare the response of snow hydrology to climate change and snow cover regime, together with longterm meteorological and hydrological observations, in the headwater of Irtysh River, Chinese Altai Mountains during 1961-2015. The average δ^(18) O values of rainfall, snowfall, meltwater, groundwater and river water for 2014–2015 hydrological year were-10.9‰,-22.3‰,-21.7‰,-15.7‰ and-16.0‰, respectively.The results from stable isotopes, snow melting observation and remote sensing indicated that the meltwater effect on hydrological processes in Kayiertesi River Basin mainly occurred during snowmelt supplying period from April to June. The contribution of meltwater to runoff reached 58.1% during this period, but rainfall, meltwater and groundwater supplied 49.1%, 36.9% and 14.0% of water resource to annual runoff, respectively. With rising air temperature and increasing snowfall in cold season, the snow water equivalent(SWE) had an increasing trend but the snow cover duration declined by about one month including 13-day delay of the first day and 17-day advancement of the end day during 1961–2016. Increase in SWE provided more available water resource. However, variations in snow cover timing had resulted in redistribution of surface water resource, represented by an increase of discharge percentage in April and May, and a decline in Juneand July. This trend of snow hydrology will render a deficit of water resource in June and July when the water resource demand is high for agricultural irrigation and industrial manufacture.

Risk prevention and control strategies for the severely affected areas of snow disaster in the Three Rivers Source Region(TRSR), China

Historically,frequent and heavy snow disaster(SD)has caused serious livestock death and casualties,resulting in a devastating impact on animal husbandry development in the Three Rivers Source Region(TRSR).From winter in 2018 to spring in 2019,the largest SD occurred in this area over the past 10 years,especially in core zones of the Lancang River Source Region.Field research results show that the main causes of the major SD include weak infrastructure(i.e.,roads,communications,warm sheds,and insufficient forage reserve),low rate of domestic animals for sale before the SD,and low loss settlement rate.SD occurrence could furtherly reduce the ability of disaster prevention,mitigation and relief of disaster loss.In the future,heavily affected SD areas should improve the forecasting ability of snowfall incidents,strengthen infrastructure construction,implement grass and livestock balance strategies,optimize livestock structure,improve loss settlement rate,and develop a modern compound model of animal husbandry development model that combines breeding,slaughtering and deep processing of animal product.

MODIS observed snow cover variations in the Aksu River Basin, Northwest China

A major proportion of discharge in the Aksu River is contributed from snow-and glacier-melt water.It is therefore essential to understand the cryospheric dynamics in this area for water resource management.The MODIS MOD10A2 remotesensing database from March 2000 to December 2012 was selected to analyze snow cover changes.Snow cover varied significantly on a temporal and spatial scale for the basin.The difference of the maximum and minimum Snow Cover Fraction(SCF)in winter exceeded 70%.On average for annual cycle,the characteristic of SCF is that it reached the highest value of 53.2%in January and lowest value of 14.7%in July and the distributions of SCF along with elevation is an obvious difference between the range of 3,000 m below and 3,000 m above.The fluctuation of annual average snow cover is strong which shows that the spring snow cover was on the trend of increasing because of decreasing temperatures for the period of 2000-2012.However,temperature in April increased significantly which lead to more snowmelt and a decrease of snow cover.Thus,more attention is needed for flooding in this region due to strong melting of snow.

Snowline and Snow Cover Monitoring at High Spatial Resolution in a Mountainous River Basin Based on a Timelapse Camera at a Daily Scale

Snowline change and snow cover distribution patterns are still poorly understood in steep alpine basins of the Qilian Mountainous region because fast changes in snow cover cannot be observed by current sensing methods due to their short time scale. To address this issue of daily snowline and snow cover observations, a groundbased EOS 7D camera and four infrared digital hunting video cameras(LTL5210A) were installed around the Hulugou river basin(HRB) in the Qilian Mountains along northeastern margin of the Tibetan Plateau(38°15'54 "N, 99°52'53" E) in September 2011.Pictures taken with the EOS 7D camera were georeferenced and the data from four LIL5210 A cameras and snow depth sensors were used to assist snow cover estimation. The results showed that the time-lapse photography can be very useful and precise for monitoring snowline and snow cover in mountainous regions. The snowline and snow cover evolution at this basin can be precisely captured at daily scale. In HRB snow cover is mainly established after October, and the maximum snow cover appeared during February and March. The consistent rise of the snowline and decrease in snow cover appeared after middle part of March. This melt process is strongly associated with air temperature increase.

Snow disaster characteristics in Palongzangbu River Basinand mitigation countermeasures for road engineering

The Palongzangbu River Basin contains the highest number of maritime province glaciers in China. There are 130 glacial lakes, 64 snow avalanche sites and 28 glacial debris flow gullies distributed within the basin. Snow disasters play a controlling role in the Sichuan-Tibet Highway construction, due to the terrain’s special characteristics of high altitude and large height differential. Segmentation mitigation countermeasures for the Sichuan-Tibet Highway are presented based on snow disaster severity level and damage mode of the road. In the Ranwu to Midui section, snow avalanches are regional disasters, so the line should be placed in sunny slopes. In the Midui Gully to Yupu section, the line should be placed in shady slopes and at higher elevations to reduce the risk of glacial lake outburst. In the Yupu to Guxiang section, all three snow disasters are minimal. In the Guxiang to Tongmai section, glacier debris flows are the major threat, thus the road should be placed in shady slopes.

The Variability of the Snow and Ice Melt in Alpine Rivers in Northwestern China

The study of snow and ice melt(SIM) is important in water-scarce arid regions for the assessment of water supply and quality. These studies involve unique difficulties, especially in the calibration of hydro-models because there is no direct way to continuously measure the SIM at hydrostations. The recursive digital filter(RDF) and the isotopic hydro-geochemical method(IHM) were coupled to separate the SIM from eight observed series of alpine streamflows in northwestern China.Validation of the calibrated methods suggested a good capture of the SIM characteristics with fair accuracy in both space and time. Applications of the coupled methods in the upper reaches of the Hei River Basin(HRB) suggested a double peak curve of the SIM fraction to streamflow for the multi-component recharged(MCR) rivers, while a single peak curve was suggested for the rainfall-dominant recharged(RDR)rivers. Given inter-annual statistics of the separation,both types of the alpine rivers have experienced an obvious decrease of SIM since 1960 s. In the past 10 years, the SIM in the two types of rivers has risen to the levels of the 1970 s, but has remained lower than the level of the 1960 s. The study provided a considerable evidence to quantify the alpine SIM based on the separation of observed data series at gauge stations. Application of the coupled method could be helpful in the calibration and validation of SIM-related hydro-models in alpine regions.

SNOW COVER MONITORING BY REMOTE SENSING AND SNOWMELT RUNOFF CALCULATION IN THE UPPER HUANGHE RIVER BASIN

The upper Huanghe(Yellow) River basin is situated in the northeast of the Qinghai Xizang(Tibet)Plateau of China. The melt water from the snow cover is main water supply for the rivers in the region during springtime and other arid regions of the northwestern China, and the hydrological conditions of the rivers are directly controlled by the snowmelt water in spring. So snowmelt runoff forecast has importance for hydropower, flood prevention and water resources utilization. The application of remote sensing and Geographic Information System (GIS) techniques in snow cover monitoring and snowmelt runoff calculation in the upper Huanghe River basin are introduced amply in this paper. The key parameter-snow cover area can be computed by satellite images from multi platform, multi temporal and multi spectral. A cluster of snow cover data can be yielded by means of the classification filter method. Meanwhile GIS will provide relevant information for obtaining the parameters and also for zoning. According to the typical samples extracting snow covered mountainous region, the snowmelt runoff calculation models in the upper Huanghe River basin are presented and they are mentioned in detail also. The runoff snowmelt models based on the snow cover data from NOAA images and observation data of runoff, precipitation and air temperature have been satisfactorily used for predicting the inflow to the Longyangxia Reservoir , which is located at lower end of snow cover region and is one of the largest reservoirs on the upper Huanghe River, during late March to early June. The result shows that remote sensing techniques combined with the ground meteorological and hydrological observation is of great potential in snowmelt runoff forecasting for a large river basin. With the development of remote sensing technique and the progress of the interpretation method, the forecast accuracy of snowmelt runoff will be improved in the near future. Large scale extent and few stations are two objective reality situations in China, so they should be considered in simulation and forecast. Apart from dividing, the derivation of snow cover area from satellite images would decide the results of calculating runoff. Field investigation for selection of the learning samples of different snow patterns is basis for the classification.

Remote Sensing Data Application to Monitor Snow Cover Variation and Hydrological Regime in a Poorly Gauged River Catchment—Northern Pakistan

Snow- and glacier-nourished river basins located in the Himalaya-Karakoram-Hindukush (HKH) ranges supply a significant amount of discharge in River Indus upstream Tarbela Dam. It is, hence, important to comprehend the cryosphere variation and its relationship to the stream flow in these high-altitude river catchments. The MODIS remotely sensed database of snow products was chosen to examine the average annual snow and glacier cover (cryosphere) variations in the Shigar River basin (poorly gauged mountainous sub-catchment of the Indus River). Hydrological regime in the area was investigated through monthly database of observed stream fluxes and climate variables (precipitation and mean temperature) for the Shigar River catchment. Analysis indicated the usefulness of remote sensing techniques for estimation of the snow cover variation in the poorly or un-gauged high-elevation catchments of the HKH zone. Results also showed that Shigar River discharge was influenced mainly by the seasonal and annual snow cover area (SCA) variation and the temperature seasonality. Moreover, it is important to uncover such inter-relationship of stream flow, climate variables and snow cover in the poorly gauged high-altitude catchments of Karakoram region for better water resource management and accurate flood hazards predictions at Tarbela.

Distribution of winter-spring snow over the Tibetan Plateau and its relationship with summer precipitationin Yangtze River

The distribution of winter-spring snow cover over the Tibetan Plateau （TP） and its relationship with summer precipitation in the middle and lower reaches of Yangtze River Valley （MLYRV） during 2003-2013 have been investigated with the moderate-resolution imaging spectrometer （MODIS） Terra data （MOD10A2） and precipitation observations. Results show that snow cover percentage （SCP） remains approximately 20% in winter and spring then tails off to below 5% with warmer temperature and snow melt in summer. The lower and highest percentages present a declining tendency while the middle SCP exhibits an opposite variation. The maximum value appears from the middle of October to March and the minimum emerges from July to August. The annual and winter-spring SCPs present a decreasing tendency. Snow cover is mainly situated in the periphery of the plateau and mountainous regions, and less snow in the interior of the plateau, basin and valley areas in view of snow cover frequency （SCF） over the TP. Whatever annual or winter-spring snow cover, they all have remarkable declining tendency during 2003-2013, and annual snow cover presents a decreasing trend in the interior of the TP and increasing trend in the periphery of the TP. Hie multi-year averaged eight-day SCP is negatively related to mean precipitation in the MLYRV. Spring SCP is negatively related to summer precipitation while winter SCP is positively related to summer precipitation in most parts of the MLYRV. Hence, the influence of winter snow cover on precipitation is much more significant than that in spring on the basis of correlation analysis. The oscillation of SCF from southeast to northwest over the TP corresponds well to the beginning,development and cessation of the rain belt in eastern China.

基于积雪数据的HBV模型改进及应用

大渡河流域内站点分布较少,历史观测数据不足,给该地区的融雪径流预报带来困难。基于欧洲中期天气预报中心提供的最新一代高分辨率陆面再分析数据集ERA5-Land,将积雪覆盖率和积雪平均深度引入度日因子雪量计算公式中,对HBV模型的积融雪模块进行改进,以提升融雪径流计算的可靠性。以大渡河上游为研究对象,选取1961—2018年的水文气象资料对模型进行率定和验证,并以2019年为例进行试预报研究。结果表明,通过引入ERA5-Land再分析数据,以及对积融雪模块进行改进,发挥了其在模拟积融雪上的优势,有效提升了融雪径流预报精度,对大渡河流域具有适用性。研究成果可为稀缺资料地区融雪径流模拟预报提供经验。

基于SRTM的SNOW-TOPMODEL模型在玛纳斯河流域的应用

基于分辨率90m网格的玛河流域SRTM数据,提取了玛纳斯河流域的河网和地形指数,生成的河网与实际河网基本接近。将加入融雪模块TOPMODE应用于玛河流域,对模型计算结果进行误差评价并分析误差产生的原因。由玛纳斯河流域应用加入融雪模块的TOPMODEL模型可以看出,该模型在玛纳斯河流域有较好的适应性,可以将此模型应用于玛纳斯河流域的洪水预报。

Uncertainties of snow cover extraction caused by the nature of topography and underlying surface

Manas River,the largest inland river to the north of the Tianshan Mountains,provides important water resources for human production and living.The seasonal snow cover and snowmelt play essential roles in the regulation of spring runoff in the Manas River Basin（MRB）.Snow cover is one of the most significant input parameters for obtaining accurate simulations and predictions of spring runoff.Therefore,it is especially important to extract snow-covered area correctly in the MRB.In this study,we qualitatively and quantitatively analyzed the uncertainties of snow cover extraction caused by the terrain factors and land cover types using TM and DEM data,along with the Per（the ratio of the difference between snow-covered area extracted by the Normalized Difference Snow Index（NDSI） method and visual interpretation method to the actual snow-covered area） and roughness.The results indicated that the difference of snow-covered area extracted by the two methods was primarily reflected in the snow boundary and shadowy areas.The value of Per varied significantly in different elevation zones.That is,the value generally presented a normal distribution with the increase of elevation.The peak value of Per occurred in the elevation zone of 3,700–4,200 m.Aspects caused the uncertainties of snow cover extraction with the order of sunny slope〉semi-shady and semi-sunny slope〉shady slope,due to the differences in solar radiation received by each aspect.Regarding the influences of various land cover types on snow cover extraction in the study area,bare rock was more influential on snow cover extraction than grassland.Moreover,shrub had the weakest impact on snow cover extraction.

Measuring Ice Thicknesses along the Red River in Canada Using RADARSAT-2 Satellite Imagery

The spring flood of 2009 in the Red River Valley was exacerbated with severe ice breakup and ice jamming. To assist ice jam mitigation by cutting and breaking up the river ice cover before the flood season and to support the operation of the Red River Floodway, Manitoba Water Stewardship is striving to model the occurrence of ice breakup and simulate the behaviour of ice jamming along the river. An important parameter in ice breakup forecasting is the ice thickness. RADARSAT-2 standard satellite images were collected along the course of the Red River in Manitoba during the 2009-2010 winter to help determine ice thicknesses along the river. Standard images can have transmit-receive polarizations in the horizontal-horizontal (HH) or horizontal-vertical (HV) configurations. Ice thickness measurements were taken in the field during the same time frame when the satellite passed over the Red River Valley. Good correlations were obtained between the HH-backscatter readings and the surveyed ice thicknesses. HV-backscatter readings correlate better with fresh snow depth measurements. Additionally, using same sensor incident angle and flight geometry allows ice thickening rate behavior over the course of the winter to be determined.

Projection of future streamflow of the Hunza River Basin,Karakoram Range(Pakistan)using HBV hydrological model

Hydrologiska Byrans Vattenbalansavdeling(HBV) Light model was used to evaluate the performance of the model in response to climate change in the snowy and glaciated catchment area of Hunza River Basin. The study aimed to understand the temporal variation of streamflow of Hunza River and its contribution to Indus River System(IRS). HBV model performed fairly well both during calibration(R2=0.87, Reff=0.85, PBIAS=-0.36) and validation(R2=0.86, Reff=0.83, PBIAS=-13.58) periods on daily time scale in the Hunza River Basin. Model performed better on monthly time scale with slightly underestimated low flows period during bothcalibration(R2=0.94, Reff=0.88, PBIAS=0.47) and validation(R2=0.92, Reff=0.85, PBIAS=15.83) periods. Simulated streamflow analysis from 1995-2010 unveiled that the average percentage contribution of snow, rain and glacier melt to the streamflow of Hunza River is about 16.5%, 19.4% and 64% respectively. In addition, the HBV-Light model performance was also evaluated for prediction of future streamflow in the Hunza River using future projected data of three General Circulation Model(GCMs) i.e. BCC-CSM1.1, CanESM2, and MIROCESM under RCP2.6, 4.5 and 8.5 and predictions were made over three time periods, 2010-2039, 2040-2069 and 2070-2099, using 1980-2010 as the control period. Overall projected climate results reveal that temperature and precipitation are the most sensitiveparameters to the streamflow of Hunza River. MIROC-ESM predicted the highest increase in the future streamflow of the Hunza River due to increase in temperature and precipitation under RCP4.5 and 8.5 scenarios from 2010-2099 while predicted slight increase in the streamflow under RCP2.6 during the start and end of the 21 th century. However, BCCCSM1.1 predicted decrease in the streamflow under RCP8.5 due to decrease in temperature and precipitation from 2010-2099. However, Can ESM2 predicted 22%-88% increase in the streamflow under RCP4.5 from 2010-2099. The results of this study could be useful for decision making and effective future strategic plans for water management and their sustainability in the region.

An Artificial Neural Network-Based Snow Cover Predictive Modeling in the Higher Himalayas

With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network(ANN), was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from- HadCM3, a global circulation model to project future climate scenario, under the A1 B emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2011 to 2040. The 4700 m to 5200 m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantial changes due to the impact of climate change.

Contrasting Historical and Recent Breakup Styles on the Meade River of Arctic Alaska in the Context of a Warming Climate

Although data for temporal spring river ice breakup are available for a number of Arctic rivers, there is a paucity of information related to the type of breakup. The Arctic Climate Impact Assessment (ACIA) of 2005 predicted a transition from mechanical to thermal spring breakup of ice cover on arctic rivers, with this shift being greatest in exclusively Arctic watersheds where observed warming is most pronounced. We describe a rare instance of an entirely Arctic river with limited but well documented historical and recent data regarding the type of breakup. Time-series ground imagery of spring breakup from 1966, 1975, 1978, 2009, 2010 and 2012, incombination with interviews of local inhabitants, documents a shift from predominantly mechanical to predominantly thermal breakup after spring 1978 and by spring 2009 within the context of a locally and regionally warming Arctic. The resultant shift from predominantly mechanical to predominantly thermal breakup is predicted to result in significant changes to water, sediment, nutrient and organic carbon fluxes, as well as riparian ecology and human activities.

陆地冰冻圈水文过程的研究现状及展望

在全球变暖的背景下,作为固态淡水资源库的冰冻圈发生剧烈变化,进而直接影响流域水文过程.因此,冰冻圈水文的研究对于流域水资源利用与管理具有重要意义.近年来,冰冻圈普遍呈现退缩趋势,主要表现为:冰川面积减小、物质亏损,冰川径流增加;积雪面积和雪水当量减小,积雪融化时间提前;多年冻土面积减小、活动层增厚、季节性冻土最大冻结深度和冻结时间减小,但是其对径流的影响效应仍然存在争议.目前研究仍存在数据具有较大的不确定性、对冰冻圈水文内在机制的物理过程认识不足,以及缺乏考虑完整冰冻圈要素的系统研究等几个方面的不足.未来关于新型观测技术手段的研发、包含完整冰冻圈水文过程的物理模型的研发及其应用,将是冰冻圈水文学研究的重点.

祁连山讨赖河流域上游积雪时空分布及其变化研究

积雪是冰冻圈的重要组成部分,在水文循环和能量平衡中起着重要的作用。积雪时空分布及其变化分析是研究内陆河流域出山径流形成、分布及变异的前提。论文以祁连山讨赖河流域上游为研究区,采用降尺度方法获取高分辨率雪深数据,并基于Sen斜率法、敏感性分析和贡献率计算方法,分析2002—2018年间雪深时空变化,揭示积雪对地形及气候等因子的响应规律。结果表明:讨赖河流域上游雪深介于0~2.50 cm之间,变率介于-0.19~0.06 cm·a^(-1)之间,域内雪深减小面积占比68.30%。雪深随海拔的增大而增加,以海拔2 500 m为界发生增减变化,高海拔地区呈减小趋势;雪深随坡度增加则呈先增后减的趋势;各坡向雪深均呈减小态势,西北坡尤为显著。从敏感性均值来看,气温和辐射对雪深具负向抑减效应,降水则具正向促增效应。高海拔区域降水对积雪变化的贡献率相对较大;低海拔河谷地带气温对积雪变化的贡献更为显著。本研究为内陆河流域上游积雪动态研究提供了范例,对出山径流模拟、预测以及流域水资源管理具有一定参考价值。

1979—2021年雅鲁藏布江流域雪深时空特征研究

雅鲁藏布江流域地处高原寒区,研究该地区的积雪时空分布对工程建设和雪灾防治有重要意义。基于1979—2021年中国雪深长时间序列数据集、中国逐月气温与降水量数据集,采用Mann-Kendall趋势分析法和皮尔森相关分析法研究雅江流域的积雪深度特性。结果表明:积雪深度在空间分布上呈现“两端高、中间低”,西部源头处与东北部边缘地区有深厚积雪覆盖;积雪主要分布于海拔4000~5200 m、坡度小于35°范围内,其中最高雪深值分布在约4850 m;1979—2021年平均雪深以0.032 cm/a的速率减少,2017年经历了43年间的最低雪深值,近几年逐渐回升到多年平均水平;年平均气温以约0.024℃/a的速度升高,其与雪深呈现显著负相关性。

GNSS反射信号陆面遥感应用综述

全球卫星导航系统(GNSS)发展至今已有半个多世纪,不仅可以为用户提供导航、定位和授时(PNT)服务,还可用于地球遥感,其应用超乎想象.本文基于GNSS反射信号(GNSS-R)的应用,系统介绍了GNSS-R的基本概念和信号特征,重点针对陆面遥感应用进行了综述,并就未来可能的发展方向做出分析,可为GNSS-R的应用提供一个重要的参考.