PDF(8426 KB)
Distribution Characteristics of Snow Cover in Typical Headwater Regions of Qinghai-Xizang Plateau
WU Nan, ZHENG Yong-sheng, CHEN Lian-gang, SHI Zhao-yun, WANG Rui, ZHANG Hai-long
Journal of Changjiang River Scientific Research Institute ›› 2026, Vol. 43 ›› Issue (8) : 222-231.
PDF(8426 KB)
PDF(8426 KB)
Distribution Characteristics of Snow Cover in Typical Headwater Regions of Qinghai-Xizang Plateau
[Objective] The Qinghai-Xizang Plateau and its surrounding headwater regions constitute the source areas of major rivers, including the Yellow River, Yangtze River, and Yarlung Zangbo River, and play a critical role in downstream water resource regulation and ecological security. As an essential component of the cold-region hydrological cycle, snow cover exerts a key influence on river runoff, dry-season water supply, and ecosystem stability through its accumulation and melt processes. This study aims to characterize the spatial heterogeneity of snow cover changes across different headwater regions and to provide a scientific basis for water resource management and ecological protection in cold regions. [Methods] Based on snow depth and meteorological datasets from 1979 to 2020, this study systematically investigated the spatial distribution, interannual variability, and climatic controls of snow cover indicators, including snow start date (SSD), snow end date (SED), snow duration (SDs), mean snow depth (SD), and maximum snow depth (MSD), across nine representative headwater basins. Linear regression was used to estimate temporal trend slopes for each variable, and the Mann-Kendall (MK) test was applied to assess trend significance. To ensure spatial consistency between meteorological and snow datasets, all meteorological variables were resampled to match the spatial resolution of snow depth data using bilinear interpolation. To quantitatively characterize the combined effects of precipitation and temperature on snow cover changes, structural equation modeling (SEM) was employed to analyze the pathways linking climatic factors to snow cover indices. [Results] (1) Snow cover exhibited pronounced spatial heterogeneity. High-elevation headwater regions were characterized by earlier snow onset, later snow end, longer duration, and greater and more stable snow depth, whereas low-latitude or warmer basins showed shorter and more intermittent snow cover with shallow snow depth and high sensitivity to temperature. The spatial patterns of SDs and SD were highly consistent, with high values concentrated in cold, high-altitude headwater regions, while snow cover in low-altitude and warmer basins was more vulnerable and more sensitive to climate change.(2) Long-term snow evolution exhibited clear regional differences. Most regions showed earlier SSD, later SED, and an overall extension of the snow season. However, unstable regions such as central NX, western JYQ, and central ZMD exhibited delayed SSD and advanced SED, indicating a shortened snow season and a pronounced warming signal. SDs generally followed the trends of SSD and SED, whereas both SD and MSD decreased across nearly all sub-basins, indicating that snow depth was most sensitive to warming. In some basins, an “extended snow duration but reduced snow depth” pattern was observed, suggesting that snow duration and snow depth were governed by different climatic controls.(3) Climatic factors significantly influenced snow cover characteristics. Increased precipitation primarily prolonged snow duration by enhancing snow accumulation and persistence, whereas rising temperature mainly reduced snow depth by accelerating melt processes and suppressing solid precipitation accumulation. The combined effects of precipitation and temperature led to strong spatial heterogeneity in snow cover changes, with notable snow depth declines in high-elevation headwater regions, while some regions with increasing precipitation exhibited longer snow duration but continued reductions in snow depth. [Conclusion] This study systematically reveals the spatial patterns, long-term evolution, and climatic drivers of snow cover across nine major headwater regions of the Qinghai-Xizang Plateau and its surroundings, providing a scientific basis for water resource management, ecological protection, and climate change adaptation in cold-region headwaters.
snow cover characteristics / spatiotemporal variations / climatic drivers / trend analysis / Qinghai-Xizang Plateau / headwater region
| [1] |
|
| [2] |
|
| [3] |
|
| [4] |
. The phase of precipitation when it reaches the ground is a first-order driver of hydrologic processes in a watershed. The presence of snow, rain, or mixed-phase precipitation affects the initial and boundary conditions that drive hydrological models. Despite their foundational importance to terrestrial hydrology, typical phase partitioning methods (PPMs) specify the phase based on near-surface air temperature only. Our review conveys the diversity of tools available for PPMs in hydrological modeling and the advancements needed to improve predictions in complex terrain with large spatiotemporal variations in precipitation phase. Initially, we review the processes and physics that control precipitation phase as relevant to hydrologists, focusing on the importance of processes occurring aloft. There is a wide range of options for field observations of precipitation phase, but there is a lack of a robust observation networks in complex terrain. New remote sensing observations have the potential to increase PPM fidelity, but generally require assumptions typical of other PPMs and field validation before they are operational. We review common PPMs and find that accuracy is generally increased at finer measurement intervals and by including humidity information. One important tool for PPM development is atmospheric modeling, which includes microphysical schemes that have not been effectively linked to hydrological models or validated against near-surface precipitation-phase observations. The review concludes by describing key research gaps and recommendations to improve PPMs, including better incorporation of atmospheric information, improved validation datasets, and regional-scale gridded data products. Two key points emerge from this synthesis for the hydrologic community: (1) current PPMs are too simple to capture important processes and are not well validated for most locations, (2) lack of sophisticated PPMs increases the uncertainty in estimation of hydrological sensitivity to changes in precipitation phase at local to regional scales. The advancement of PPMs is a critical research frontier in hydrology that requires scientific cooperation between hydrological and atmospheric modelers and field scientists.
|
| [5] |
Seasonal snowmelt water from mountainous areas is critical for water supply in arid regions. Snowmelt profoundly affects the parameterization of the Budyko framework, which describes the long-term relationship between precipitation and runoff. This is true in Xinjiang, a representative arid region in Northwest China. However, the effects of snowmelt water on the water balance in this region remain unclear. Based on observed runoff data in 64 catchments of Xinjiang during 2000–2010, we analyzed the effects of meltwater in the local water balance both spatially and temporally through the Budyko curve and redundancy analysis (RDA) methods, and then investigated the influences of changing meltwater on runoff. Inclusion of snowmelt water into the item of the water availability significantly improved the performance of the Budyko equation for predicting runoff. The results of RDA showed that snowmelt water, potential evaporation (PET), and rainfall combined explained 66% of the spatial variations in runoff, while the individual effects of snowmelt water, PET, and rainfall were 19%, 13%, and 1%, respectively, with the interactions among the three variables being 16%. These results suggest that the accelerating changes of meltwater due to climate warming will significantly alter the regimes of runoff in these regions.
|
| [6] |
|
| [7] |
IPCC. Climate Change 2023[R]. Geneva, Switzerland: IPCC, 2023.
|
| [8] |
A study is presented of the geographical distribution and spatial and temporal variabilities of the western China snow cover in the past 47 yr between 1951 and 1997. The data used consist of Scanning Multichannel Microwave Radiometer (SMMR) 6-day snow-depth charts, NOAA weekly snow extent charts, and the daily snow depth and number of snow cover days from 106 selected meteorological stations across western China. Empirical orthogonal function was performed on the SMMR dataset to better understand the spatial pattern and variability of the Qinghai–Xizang (Tibet) snow cover. A multiple linear regression analysis was conducted to show the association of interannual variations between snow cover and snow season temperature as well as precipitation. Further, the autoregressive moving average model was fitted to the snow and climate time series to test for their long-term trends. Results show that western China did not experience a continual decrease in snow cover during the great warming period of the 1980s and 1990s. It is of interest to note that no correlation was identified between temperature and precipitation in the snow cover season. However, year-to-year fluctuation of snow cover responds to both snowfall and snow season temperature. About one-half to two-thirds of the total variance in snow cover is explained by the linear variations of snowfall and snow season temperature. The long-term variability of western China snow cover is characterized by a large interannual variation superimposed on a small increase trend. The positive trend of the western China snow cover is consistent with increasing snowfall, but is in contradiction to regional warming. In addition, many constraints of the Qinghai–Xizang (Tibet) snow cover force the author’s challenge of Blanford’s hypothesis.
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
张建云, 刘九夫, 金君良, 等. 青藏高原水资源演变与趋势分析[J]. 中国科学院院刊, 2019, 34(11):1264-1273.
(
|
| [15] |
王奕钧, 于德永, 周祉蕴. 青藏高原寒区水文过程研究进展及模型述评[J]. 冰川冻土, 2024, 46(4): 1312-1328.
全球气候变暖导致冰冻圈发生了剧烈变化，突出表现为冰川退缩、积雪消融、冻土退化等，加速了固态水资源向液态水资源的转化，打破了青藏高原寒区水资源分配的长期稳定机制。本文基于寒区冰冻圈变化水文效应角度，梳理了气候变化下青藏高原寒区水文过程研究进展，剖析了当前青藏高原寒区水文过程研究中存在的热点和难点。由于水文模型为研究水文循环过程的重要工具，模型的结构和功能决定了水文过程研究的准确度和发展方向。因此，本文总结了10个典型水文模型在模拟高原寒区冰川、融雪径流算法的优点和局限性及模型冰川、积雪、冻土模块特点，凝练总结了影响高原寒区水文过程模型模拟精度的主要问题。结果表明：青藏高原寒区气象观测站点资料的匮乏加大了数据输入、参数估计的不确定性；对冰冻圈水文内在物理过程认识不足导致了模型结构的不完备性，进而影响水文过程模拟精度。最后，本文从多元数据联合使用、机器学习应用等方面探讨了如何提高水文模型精度，为寒区水文模型的改进和完善提供参考。
(
Global warming has triggered significant changes in the cryosphere， manifested in phenomena such as glacier retreat， snowmelt， and permafrost degradation. These transformations accelerate the conversion of solid water resources into liquid water， disrupting the long-term stability of water resource allocation in the Qinghai-Xizang Plateau’s cold regions. This paper takes the perspective of hydrological effects of cryosphere changes in cold regions， reviewing recent advancements in the understanding of hydrological processes under climate change in the Qinghai-Xizang Plateau. We analyze the current challenges and hotspots in hydrological research specific to the Qinghai-Xizang Plateau. Given that hydrological modeling is a crucial tool for studying the hydrological cycle， the structure and functionality of these models significantly influence the accuracy and direction of hydrological research. This paper summarizes the advantages and limitations of hydrological model algorithms for simulating glacier and snowmelt runoff in the plateau cold regions， and the characteristics of glacier， snow， and permafrost modules in 10 typical hydrological models. We also distill the main issues affecting the simulation accuracy of hydrological process models in this region. Our findings indicate that the limited availability of meteorological observation stations in the Qinghai-Xizang Plateau contributes to uncertainties in data input and parameter estimation. Moreover， a lack of comprehensive understanding of the intrinsic physical mechanisms governing hydrological processes in the cryosphere results in incomplete model structures， further impacting the simulation accuracy of these hydrological models. Finally， we discuss strategies for enhancing the accuracy of hydrological models through the integrated application of multivariate data and machine learning algorithms in cold regions. |
| [16] |
|
| [17] |
董宁澎, 王浩, 杨明祥, 等. 考虑融雪及土壤冻融的新安江模型及其应用: 以雅砻江上游径流模拟为例[J]. 水科学进展, 2024, 35(4): 530-542.
(
|
| [18] |
|
| [19] |
白淑英, 吴奇, 史建桥, 等. 青藏高原积雪深度时空分布与地形的关系[J]. 国土资源遥感, 2015, 27(4):171-178.
(
|
| [20] |
吴南, 张珂, 管晓祥, 等. 1979-2018年雅砻江中上游积雪时空变化及影响因素分析[J]. 水资源保护, 2022, 38(5):151-158.
(
|
| [21] |
李栋梁, 刘玉莲, 于宏敏, 等. 1951-2006年黑龙江省积雪初终日期变化特征分析[J]. 冰川冻土, 2009, 31(6): 1011-1018.
(
|
| [22] |
|
| [23] |
|
| [24] |
. Snow cover on the Qinghai–Tibetan Plateau (QTP) plays a significant role in the global climate system and is an important water resource for rivers in the high-elevation region of Asia. At present, passive microwave (PMW) remote sensing data are the only efficient way to monitor temporal and spatial variations in snow depth at large scale. However, existing snow depth products show the largest uncertainties across the QTP. In this study, MODIS fractional snow cover product, point, line and intense sampling data are synthesized to evaluate the accuracy of snow cover and snow depth derived from PMW remote sensing data and to analyze the possible causes of uncertainties. The results show that the accuracy of snow cover extents varies spatially and depends on the fraction of snow cover. Based on the assumption that grids with MODIS snow cover fraction > 10 % are regarded as snow cover, the overall accuracy in snow cover is 66.7 %, overestimation error is 56.1 %, underestimation error is 21.1 %, commission error is 27.6 % and omission error is 47.4 %. The commission and overestimation errors of snow cover primarily occur in the northwest and southeast areas with low ground temperature. Omission error primarily occurs in cold desert areas with shallow snow, and underestimation error mainly occurs in glacier and lake areas. With the increase of snow cover fraction, the overestimation error decreases and the omission error increases. A comparison between snow depths measured in field experiments, measured at meteorological stations and estimated across the QTP shows that agreement between observation and retrieval improves with an increasing number of observation points in a PMW grid. The misclassification and errors between observed and retrieved snow depth are associated with the relatively coarse resolution of PMW remote sensing, ground temperature, snow characteristics and topography. To accurately understand the variation in snow depth across the QTP, new algorithms should be developed to retrieve snow depth with higher spatial resolution and should consider the variation in brightness temperatures at different frequencies emitted from ground with changing ground features.
|
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
. Reliable precipitation data are highly necessary for geoscience\nresearch in the Third Pole (TP) region but still lacking, due to the complex\nterrain and high spatial variability of precipitation here. Accordingly,\nthis study produces a long-term (1979–2020) high-resolution (1/30∘, daily) precipitation dataset (TPHiPr) for the TP by merging the\natmospheric simulation-based ERA5_CNN with gauge observations\nfrom more than 9000 rain gauges, using the climatologically aided interpolation\nand random forest methods. Validation shows that TPHiPr is generally\nunbiased and has a root mean square error of 5.0 mm d−1, a\ncorrelation of 0.76 and a critical success index of 0.61 with respect to 197\nindependent rain gauges in the TP, demonstrating that this dataset is\nremarkably better than the widely used datasets, including the latest generation of reanalysis (ERA5-Land), the state-of-the-art\nsatellite-based dataset (IMERG) and the multi-source merging datasets\n(MSWEP v2 and AERA5-Asia). Moreover, TPHiPr can better detect\nprecipitation extremes compared with these widely used datasets. Overall,\nthis study provides a new precipitation dataset with high accuracy for the\nTP, which may have broad applications in meteorological, hydrological and\necological studies. The produced dataset can be accessed via\nhttps://doi.org/10.11888/Atmos.tpdc.272763 (Yang and Jiang, 2022).
|
| [29] |
|
| [30] |
Long-term snow depth/snow water equivalent (SWE) products derived from passive microwave remote sensing data are fundamental for climatological and hydrological studies. However, the temporal continuity of the products is affected by the updating or replacement of passive microwave sensors or satellite platforms. In this study, we inter-calibrated brightness temperature (Tb) data obtained from the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMI/S). Then, we evaluated the consistency of the snow cover area (SCA) and snow depth derived from the Scanning Multichannel Microwave Radiometer (SMMR), SSM/I and SSMI/S. The results indicated that (1) the spatial pattern of the SCA derived from the SMMR and SSM/I data was more consistent after calibration than before; (2) the relative biases in the SCA and snow depth in China between the SSM/I and SSMI/S data decreased from 42.42% to 1.65% and from 66.18% to −1.5%, respectively; and (3) the SCA and snow depth derived from the SSM/I data carried on F08, F11 and F13 were highly consistent. To obtain consistent snow depth and SCA products, inter-sensor calibrations between SMMR, SSM/I and SSMI/S are important. In consideration of the snow data product continuation, we suggest that the brightness temperature data from all sensors be calibrated based on SSMI/S.
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
陈恬宇, 李东峰, 王立雪, 等. 长江流域年极端气温的时空变化特征研究[J]. 华中师范大学学报(自然科学版), 2023, 57(6):837-845.
(
|
| [35] |
孙秀忠, 罗勇, 张霞, 等. 近46年来我国降雪变化特征分析[J]. 高原气象, 2010, 29(6): 1594-1601.
利用国家气象信息中心提供的各测站多年逐日降水和逐日天气现象资料， 在运用旋转经验正交函数（Rotated Empirical Orthogonal Function, REOF）和相关分析进行降雪分区的基础上， 重点分析了近46年来我国降雪的时空分布、 演变特征和长期气候趋势。结果表明， 降雪分布清楚地反映了我国的地理特征和气候特点， 即高纬度、 高海拔地区降雪多， 南方降雪地区集中。在空间上， 降雪增加的区域主要分布在新疆北部、 东北区北部、 西藏高原东部和淮河流域部分地区。在时间上， 我国平均年降雪量总体呈弱的减少趋势。在气候变暖的背景下， 除东北区北部和西北区西部降雪趋势为正值外， 其它分区都为负值， 其中长江中下游地区降雪减少最明显。Mann-Kendall检验表明， 在20世纪90年代新疆北部、 东北区北部的降雪发生了由少到多的突变， 且增加趋势显著, 其它分区没有突变发生。
(
|
/
| 〈 |
|
〉 |
