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Journal of Arid Land >

2024 , Vol. 16 >Issue 7: 877 - 894

DOI: https://doi.org/10.1007/s40333-024-0080-5

  • WANG Tongxia 1, 2 ,
  • CHEN Fulong , 1, 2, * ,
  • LONG Aihua 1, 3 ,
  • ZHANG Zhengyong 4 ,
  • HE Chaofei 1 ,
  • LYU Tingbo 1 ,
  • LIU Bo 1 ,
  • HUANG Yanhao 1
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收稿日期: 2024-02-28

  修回日期: 2024-06-07

  录用日期: 2024-06-11

  网络出版日期: 2025-08-14

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Glacier area change and its impact on runoff in the Manas River Basin, Northwest China from 2000 to 2020

  • WANG Tongxia 1, 2 ,
  • CHEN Fulong , 1, 2, * ,
  • LONG Aihua 1, 3 ,
  • ZHANG Zhengyong 4 ,
  • HE Chaofei 1 ,
  • LYU Tingbo 1 ,
  • LIU Bo 1 ,
  • HUANG Yanhao 1
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  • 1College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, China
  • 2Key Laboratory of Cold and Arid Regions Eco-Hydraulic Engineering of Xinjiang Production & Construction Corps, Shihezi 832000, China
  • 3State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
  • 4College of Sciences, Shihezi University, Shihezi 832000, China
* CHEN Fulong (E-mail: )

Received date: 2024-02-28

  Revised date: 2024-06-07

  Accepted date: 2024-06-11

  Online published: 2025-08-14

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本文引用格式

WANG Tongxia , CHEN Fulong , LONG Aihua , ZHANG Zhengyong , HE Chaofei , LYU Tingbo , LIU Bo , HUANG Yanhao . [J]. Journal of Arid Land, 2024 , 16(7) : 877 -894 . DOI: 10.1007/s40333-024-0080-5

Abstract

Understanding the distribution and dynamics of glaciers is of great significance to the management and allocation of regional water resources and socio-economic development in arid regions of Northwest China. In this study, based on 36 Landsat images, we extracted the glacier boundaries in the Manas River Basin, Northwest China from 2000 to 2020 using eCognition combined with band operation, GIS (geographic information system) spatial overlay techniques, and manual visual interpretation. We further analyzed the distribution and variation characteristics of glacier area, and simulated glacial runoff using a distributed degree-day model to explore the regulation of runoff recharge. The results showed that glacier area in the Manas River Basin as a whole showed a downward trend over the past 21 a, with a decrease of 10.86% and an average change rate of -0.54%/a. With the increase in glacier scale, the number of smaller glaciers decreased exponentially, and the number and area of larger glaciers were relatively stable. Glacier area showed a normal distribution trend of increasing first and then decreasing with elevation. About 97.92% of glaciers were distributed at 3700-4800 m, and 48.11% of glaciers were observed on the northern and northeastern slopes. The retreat rate of glaciers was the fastest (68.82%) at elevations below 3800 m. There was a clear rise in elevation at the end of glaciers. Glaciers at different slope directions showed a rapid melting trend from the western slope to the southern slope then to the northern slope. Glacial runoff in the basin showed a fluctuating upward trend in the past 21 a, with an increase rate of 0.03×108 m3/a. The average annual glacial runoff was 4.80×108 m3, of which 33.31% was distributed in the ablation season (June-September). The average annual contribution rate of glacial meltwater to river runoff was 35.40%, and glacial runoff accounted for 45.37% of the total runoff during the ablation season. In addition, precipitation and glacial runoff had complementary regulation patterns for river runoff. The findings can provide a scientific basis for water resource management in the Manas River Basin and other similar arid inland river basins.

Key words： glacier area; glacial runoff; climate change; glacier boundary extraction; distributed degree-day model; Manas River Basin

1 Introduction

Glaciers are important "solid reservoirs" in the arid regions of Northwest China, and glacial runoff in Xinjiang Uygur Autonomous Region of Northwest China accounts for more than 25.00% of runoff from the mountains, exerting a significant influence on river runoff (Li et al., 2011; Zhang et al., 2018). The advent of climate warming has precipitated a widespread retreat of glaciers, with this regressive trend markedly accelerating since the beginning of the 21st century (Bliss et al., 2014; Miles et al., 2020), which has important implications for the regional water cycle and socio-economic development (Bliss et al., 2014). Glaciers are an important freshwater resource, and their retreat has changed the distribution characteristics of water resources, which is not only crucial for industrial and agricultural development and ecological environment construction but also leads to a decline in glacier ecosystem service function. Therefore, it is of great scientific and social significance to explore glacier changes and the relationship between glaciers and river runoff within the context of global change (Huang et al., 2021; Tielidze et al., 2022).
Changes in indicators such as glacier area and glacial runoff directly reflect the process of glaciers response to climate change (Zhao et al., 2020). The study on the distribution and variation characteristics of glaciers has long been a research hotspot and frontier issue in scientific research (Sorg et al., 2012). Due to the complex and diverse topography and climatic conditions in the alpine regions, traditional glacier monitoring of a single glacier has severe limitations, and it is difficult to implement large-scale dynamic monitoring of glaciers. With the rapid development of remote sensing technology, glacier monitoring can now enable continuous observation over a wide range and long period (Xu et al., 2015), which has greatly facilitated the determination of glacier boundaries and the estimation of glacial area in larger regions (Donovan et al., 2019). Therefore, based on satellite remote sensing data, combining automatic computer classification and manual visual interpretation has become the main method for obtaining glacier information; for example, the glacier inventory data in China were extracted using this method (Guo et al., 2015; Shi et al., 2022). Although significant progress has been made in using remote sensing technology to extract glacier information, some challenges remain, such as the occlusion of high mountains, cloud interference, temporary snowfall, and other factors. These result in uncertainty in the extraction of glacier boundaries, which in turn affects the accurate assessment of glacial runoff. For example, terrain can affect the location of rock glaciers (Janke, 2013), and disturbances such as high mountains and clouds also affect sensor recognition of glacier information, leading to incomplete information on the extracted glacier boundaries and underestimation of glacier area (Yao et al., 2022). In addition, such disturbances also block solar radiation, reducing the time and intensity of sunlight hitting the surface of glaciers, thus affecting the rate of glacier melt and the magnitude of ice melt (Hirose and Marshall, 2013). Temporary snowfall is easily misclassified as a glacier, resulting in an overestimation of the glacier area to some extent, which causes a sudden increase in ice meltwater (Kumar et al., 2018). However, most studies only use single-phase imagery at a certain time point as a data source to obtain and analyze the distribution and variation characteristics of glaciers (Du et al., 2020). This approach ignores the overestimation of glacier area due to temporary snowfall and the underestimation of glacier area due to cloud and moraine cover (Wang et al., 2021). Therefore, based on multiple remote sensing images, with the help of eCognition and GIS (geographic information system) softwares, glacier boundaries can be extracted using band calculations, GIS spatial overlay techniques, and manual visual interpretation. This approach can identify subtle changes in glaciers in a more detailed way and mitigate the lack of glacier information in abnormal years. Thus, it is conducive to improving the accuracy of glacier boundaries, enabling objective analysis of year-to-year changes in glacier area and scale, mastering its changing law, and providing a scientific basis for the use of water resources in arid inland river basins.
In the arid regions of Northwest China, glacial runoff from the mountains is not only an important guarantee of ecological environmental protection but also the basis for sustainable regional socio-economic development (Yao et al., 2004; Huss and Hock, 2018). Since the climate of Northwest China tended to be warmer and wetter in the 1990s, glaciers have been retreating at an accelerated rate, with drastic losses in glacier mass and significant increases in glacial runoff (Rafiq et al., 2019). In the arid regions of Northwest China, glacial meltwater is an important recharge source for river runoff, and fluctuations in glacial runoff in different time periods cause changes in the abundance and depletion of river water (Zhang et al., 2019b). In glacial basins, changes in the contribution of glacial meltwater to river runoff may be masked by variability in precipitation (Unger-Shayesteh et al., 2013). Therefore, it is important to assess the relative contribution of glacial meltwater to river runoff. The contribution of ice storage and glacier area change to runoff can be assessed using the glacier area monitored by remote sensing technology (Ding et al., 2006). However, this method makes it difficult to comprehensively and objectively analyze the complex relationship between glaciers and runoff across the basin. Regarding factors such as changes in glacial runoff and glacial area in the basin, some scholars have used a period of glacier area data to simulate the changes in glacial runoff, without considering the characteristics of the multi-year recession of glacier area and its dynamics (Zhao et al., 2020; Su et al., 2022). In particular, glacier area is in a state of constant decline as the temperature rises, and using single-phase glacier area data to assess the glacial meltwater will result in a great deal of uncertainty and overestimate its contribution to river runoff to a certain extent. In addition, basin flooding is generally caused by melting snow and ice as well as rainfall in spring and summer; changes in basin water resources due to glacial meltwater will affect typical mountain-basin terrestrial ecosystems and agricultural production.
The Manas River originates from the northern slope of the Tianshan Mountains, and its runoff plays a decisive role in the agriculture development of the basin. Glacial meltwater is one of the important water sources in the Manas River Basin. In order to explore the contribution of glacial meltwater to river runoff, this study analyzed the distribution and variation characteristics of glacier area by considering its year-by-year changes, simulated glacial runoff using a raster-based distributed degree-day model, and explored its change trends and the regulation of runoff recharge. The findings can provide a reference for regional agricultural production, sustainable utilization of water resources, and socio-economic development in the Manas River Basin and other similar arid inland river basins.

2 Materials and methods

2.1 Study area

The Manas River Basin (43°27′-45°21′N, 85°01′-86°32′E; Fig. 1) is located in the middle of the northern slope of the Tianshan Mountains and along the southern edge of the Junggar Basin. Originating from the Eren Habirga Mountains in the south, the river basin extends to the Gurbantunggut Desert in the north. The basin is located far from the sea with sparse precipitation, typical of a continental arid climate. The average annual temperature is 5.9°C, and the average annual precipitation is 338.2 mm, mainly concentrated in spring and summer. The average annual evaporation is 1550.6 mm, and the frost-free period of is about 170 d (He et al., 2023). Due to vigorous retrogressive erosion, the basin shifts southward into the high mountains, providing conditions for glacier accumulation (Liu et al., 2009). According to the data of the Second Chinese Glacier Inventory (http://www.ncdc.ac.cn/), there are 754 glaciers in the Manas River Basin, with an area of 501.82 km2 and a glacier reserve of 39.01×108 m3, which provides sufficient water for rivers. The area below the snow line is better vegetated and rich in precipitation and is the runoff formation area. The topography of the basin is high in the southeast and low in the northwest, showing a state of inclination from the southeast to the northwest, with the river traversing from the south through the middle and high mountainous areas, alluvial fan areas, and piedmont-inclined plain areas. The Manas River is an inland river with the largest number of glaciers in the internal water system of the Junggar Basin, with a total length of about 400 km. The Manas River then flows into the Manas Lake and Kenswat Hydrological Station, before flowing out of the canyon and into the piedmont terrace (Li et al., 2022). The Kenswat Hydrological Station is the control station of Manas River runoff, with an elevation of about 900 m and a water control area of 5131.61 km2.
Fig. 1 Overview of the Manas River Basin based on the digital elevation model (DEM) and spatial distribution of glaciers based on the Second Chinese Glacier Inventory and glacier vanishing points determined in this study. The Second Chinese Glacier Inventory data were from the National Cryosphere Desert Data Center (http://www.ncdc.ac.cn/).

2.2 Data sources

The data used in this paper are described in Table 1. Landsat image data from 2000 to 2020 were used for glacier boundary extraction and glacier change area statistics in the study area. Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) data were used to divide elevation zones and extract topographic factors. Meteorological data from the European Center for Medium-Range Weather Forecast (ECMWF) Land Surface Reanalysis version 5 (ERA5-Land) with a spatial resolution of 1 km were used to characterize the spatial distribution of temperature and precipitation in mountainous areas and analyze their influence mechanisms on glacier area change and glacial runoff. The data of the Second Chinese Glacier Inventory from 2006 to 2011 were used to verify the accuracy of glacier boundaries. The observed data from the Kenswat Hydrological Station were used to verify the accuracy of meteorological data from ERA5-Land and runoff simulation results. The elevation change dataset for all glaciers on Earth was provided by Hugonnet et al. (2021). In this study, the elevation change data during 2000-2005, 2005-2010, 2010-2015, and 2015-2020 were selected to obtain data on glacier volume change.
Table 1 Detailed description of data used in the study
Data type Time
span		 Row/
Column		 Spatial resolution Data source
Landsat image data Landsat 8 OLI 2013-2020 144/030 15/30 m http://www.gscloud.cn/
Landsat 4-5 TM 2006-2011 144/030 30 m
Landsat 7 ETM_SLC_off 2003-2012 144/030 15/30 m
Landsat 7 ETM_SLC_on 2000-2002 144/030 15/30 m
ASTER GDEM - 144/030 30 m http://www.gscloud.cn/
Elevation change dataset 2000-2020 - 100 m https://doi.org/10.6096/13
ERA5-Land 2000-2020 - 1 km https://cds.climate.copernicus.eu/
Second Chinese Glacier Inventory 2006-2011 - - http://www.ncdc.ac.cn/
Observed data from the Kenswat Hydrological Station 2000-2014 - - -

Note: OLI, Operational Land Imager; TM, Thematic Mapper; ETM, Enhanced Thematic Imager; SLC, Scan Lines Corrector; ASTER GDEM, Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model; ERA5-Land, European Center for Medium-Range Weather Forecast (ECMWF) Land Surface Reanalysis version 5. "-" means no data.

2.3 Methods

2.3.1 Glacier boundary extraction

In long-term glacier research work, large-scale glacier boundaries are mainly obtained by remote sensing image interpretation, and glacier information is mostly extracted using methods such as ratio threshold and the normalized snow cover index (NDSI) (Singh et al., 2021). However, due to the interference of clouds (Fig. 2a), temporary snowfall (Fig. 2c), and mountain shadows (Fig. 2e) in remote sensing images, it is difficult to obtain accurate glacier boundaries with single-phase images. Glacier area is often overestimated due to a failure to take into account temporary snowfall in mountainous areas and underestimated due to occlusion by clouds and mountain shadows. Compared to the method of obtaining data on the glacier area by using only one image, the "global-local" overlay operation involves comprehensively considering multiple images from adjacent months in the same year or adjacent years (in this study, we chose about two or three images per year). This approach can effectively reduce interference from clouds, temporary snowfall, and mountain shadows, supplement or detect the integrity of glacier boundaries, and obtain a more accurate glacier area. In this study, with the help of eCognition software, we used the NDSI to distinguish glaciers, clouds, temporary snowfall, etc., according to the reflection characteristics of glaciers in visible light and short-wave infrared bands. A series of classification rules were established in combination with digital elevation model (DEM) to identify and extract information such as glaciers, clouds, and mountain shadows. In the process of extracting glacier information, we established reliable classification rules and parameters through many experiments. The main process is as follows:
Fig. 2 Extraction of glacier boundaries based on base images with the least cloud cover, temporary snowfall, and mountain shadow interference (a1-a3) and reference images from adjacent years that can compensate for interference from clouds, temporary snowfall, and mountain shadows in the base images (b1-b3)
(1) Through global observations, images with the least cloud cover, temporary snowfall, and mountain shadow interference were selected as the base images (Fig. 2a1-a3), while images from adjacent years that can compensate for interference from clouds, temporary snowfall and mountain shadows in the base images were selected as reference images (Fig. 2b1-b3). The above images were segmented by eCognition software with a segmentation threshold of 10.
(2) In order to improve the purity of glacier information acquisition, we used the water vapor absorption characteristic of the ninth cirrus cloud band in Landsat OLI images to identify clouds. The cloud interference information in Landsat TM/ETM images was obtained by establishing cloud detection rules that subtract the near-infrared band from the red band and add the mid-infrared band.
(3) NDSI combined with manual intervention was used to classify and extract glacier information, in which the optimal classification threshold of NDSI was 0.08-0.10.
(4) To ensure the completeness and accuracy of glacier information, we overlaid and calculated the extracted glacier boundaries in adjacent years (months) in ArcGIS, taking into consideration the image characteristics. For glacier boundaries extracted from images with large disturbances of temporary snowfall, the snow-covered areas in the base images were partially intersected with the reference images to obtain glacier boundaries of smaller size. For areas obscured by clouds, the corresponding reference images were selected for merging or replacement.
(5) With the help of the attribute assignment function of eCognition and manual visual interpretation, the glacier boundary information was improved, and the details of each glacier boundary were corrected and smoothed by ArcGIS technology. The results of glacier boundary extraction are shown in Figure 3.
Fig. 3 Spatial distribution of the extracted glaciers in the Manas River Basin and localized comparison of glacier boundaries during 2000-2020

2.3.2 Accuracy assessment of the meteorological data from EAR5-land and error analysis of glacier area

Due to the scarcity of stations in the Manas River Basin, we used the meteorological data from EAR5-land in this study, and selected 13 stations around the basin to assess the reliability of EAR5-land. Compared with the station observations, the monthly average temperature and monthly precipitation data of EAR5-land from June to September showed higher applicability in the basin, with R2 values of 0.96 and 0.79, respectively, and root mean squared error (RMSE) values of 1.23 and 11.48, respectively (Fig. 4). In addition, Tang (2023) verified the meteorological data from EAR5-land through observed data from the Kenswat Hydrological Station, concluding that the correlation for temperature was extremely high, with a relative error of -0.07. Compared with China Meteorological Forcing Dataset (CMFD) and Climate Forecast System Reanalysis (CFSR) precipitation products, precipitation frequency based on ERA5-Land was the closest to the observed value. To sum up, the meteorological data from EAR5-land can objectively reflect climate change in mountainous areas and can detailedly describe the temporal and spatial variation characteristics of temperature and precipitation in the basin.
Fig. 4 Accuracy assessment of monthly average temperature (a) and monthly precipitation (b) data from EAR5-land during June-September compared with the station observations
Glacier area errors consist of systematic errors and accidental errors (Liu et al., 1998; Paul et al., 2017). Systematic errors are mainly caused by clouds, temporary snowfall, mountain shadows, and moraines, which cannot be accurately estimated but can be reduced by selecting high-quality remote sensing images and multi-scene images from adjacent years (months), as well as by improving glacier interpretation. Accidental errors are caused by the resolution and spectral characteristics of remote sensing images, which can be estimated by error theory (Sun et al., 2018). In this study, we only considered the errors caused by the resolution of remote sensing images, and the accuracy of glacier area was tested using Equations 1 and 2.
${{\mu }_{ai}}=(p/\lambda )\sigma ({{\lambda }^{2}}/2)=\frac{\sigma }{2}\times p\lambda $,
${{\mu }_{T}}=\sqrt{\sum\limits_{i=1}^{n}{{{\mu }_{ai}}^{2}}}$,
where μai is the area error of the ith glacier (m2); p is the circumference of the glacier (m); λ is the spatial resolution of the remote sensing image (30 m); σ is the Gaussian distribution correction coefficient (σ=0.6872); μT is the area error of glaciers in the whole study area (m2); and n is the total number of glaciers. The results showed that the area error of glacier interpretation caused by the resolution of remote sensing images was ±1.88 km2, accounting for 0.04% of the total glacier area in the basin.

2.3.3 Simulation of glacier volume

Data on changes in the surface elevation of glaciers in the Manas River Basin were obtained for the periods 2000-2005, 2005-2010, 2010-2015, and 2015-2020. The glacier volume changes in the basin were obtained by considering the glacier area in the corresponding years. Glacier volume change (DV; m3) for the respective study periods was calculated using the following equation (Wang et al., 2024):
$DV=Ddh\times DS$,
where Ddh is the glacier surface elevation change for each pixel (m); and DS is the glacier area change for each pixel in the study period (km2).

2.3.4 Simulation of glacial runoff

Snow and ice meltwater and liquid precipitation are important recharge forms of river runoff, and variations in snow and ice meltwater directly affect the change in river runoff. Among the many meltwater runoff models, the degree-day model based on the linear relationship between ice and snow meltwater and air temperature has a wide range of applications due to its simple principle and reliable results (Zhang, 2006). In this study, the glacial meltwater equivalent (A; mm) was obtained by calculating the positive cumulative temperature (PDD; °C) and degree-day factor (DDF; mm/(d•°C)) of each pixel. The formula is as follows:
$A=\text{DDF}\times PDD$.
Since the annual average temperature (Tm; °C) varies as a sinusoidal function of time (Luo, 2012), PDD was calculated using formulas as follows (Zhang et al., 2012; Zhao et al., 2020).
$PDD=\int\limits_{\text{0}}^{N}{dt}\left[ \frac{\delta }{\sqrt{\text{2 }\!\!\pi\!\!\text{ }}}\text{exp}\left( \frac{{{T}_{m}}^{\text{2}}}{\text{2}{{\delta }^{\text{2}}}} \right)+\frac{{{T}_{m}}}{\text{2}}erfc\left( \frac{{{T}_{m}}}{\delta \sqrt{\text{2}}} \right) \right]$,
${{T}_{m}}={{T}_{a}}+\text{(}{{T}_{\max }}-{{T}_{a}}\text{)cos}\frac{\text{2 }\!\!\pi\!\!\text{ }}{A}$,
where N is one year; δ is the standard deviation of temperature distribution (°C); erfc is the complementary error function; Ta is the average monthly temperature (°C); and Tmax is the highest monthly temperature (°C).
Generally speaking, DDF can be directly measured by a solubility meter or ablation flower pole, or calculated by an energy balance model. Due to the lack of observation data in the Manas River Basin, it is difficult to obtain the DDF of the basin through actual measurement. Therefore, in this study, we referred to the transfer functions of DDF established by Zhang et al. (2019a). On this basis, the DDF was improved depending on the latitude, longitude, elevation, temperature, and precipitation in the Manas River Basin:
$\text{DDF}=\text{9}\text{.763}-\text{0}\text{.277Lat}+\text{0}\text{.047Lon}-\text{1}\text{.72}\times \text{1}{{\text{0}}^{-3}}Z-\text{0}\text{.62}T+\text{6}\text{.99}\times \text{1}{{\text{0}}^{-3}}P$,
where Lat, Lon, and Z are the longitude (°), latitude (°), and elevation (m) at the glacier terminus, respectively; and T and P are the annual average temperature (℃) and annual precipitation (mm) of glacier area, respectively. DDF fluctuated in the range of 2.33-4.50 mm/(d•°C), and the results are in strong agreement with those of Zhang et al. (2006, 2019a) and Zhao et al. (2020).
Therefore, data on the glacial runoff of the basin can be obtained by Equation 8 (Zhang et al., 2012):
$Q=\sum\limits_{i=\text{1}}^{n}{{{S}_{i}}\left[ \text{(1}-F\text{)}{{A}_{i}}+{{P}_{L(i)}} \right]}$,
where Q is the glacial runoff (m3); Si is the glacier area of the ith glacier (km2); F is the freezing ratio (mm) usually calculated as 10.00% of the amount melted; Ai is the glacial meltwater equivalent of the ith glacier (mm); and PL(i) is the liquid precipitation of the ith glacier (mm).

2.3.5 Determination of the glacier vanishing points

When exploring the impact of climate and environmental changes on glacier melting, previous studies have been conducted from the perspective of the study area as a whole, without quantitative analysis of its change law from the perspective of the glacier disappearance regions (Gao et al., 2010). In this study, the glacier regions in 2000 and 2020 were processed using the "symmetrical difference" method in ArcGIS to obtain the glacier disappearance regions. Following this, 67 glacier vanishing points (Fig. 1) were randomly extracted from the glacier disappearance regions to facilitate the extraction of geographical locations (such as elevation and aspect) and meteorological elements (such as temperature and precipitation) of corresponding points. Based on the spatial distribution and aggregation characteristics of glaciers in the basin, we divided the glacier disappearance regions into eastern, central, and western region to quantify and analyze the impacts of meteorological factors on glacier ablation.

3 Results

3.1 Temporal variation characteristics of glaciers in the Manas River Basin

Over the past 21 a, glacier area and volume in the Manas River Basin showed a decreasing trend, while the number of glaciers initially increased and then decreased, with a serious glacier retreat (Table 2). From 2000 to 2020, the average coverage rate of glaciers in the Manas River Basin was about 9.48%, with an overall downward trend. The total glacier area decreased from 520.12 to 463.61 km2 with a decrease of about 10.86%, and the average rate of change in the area was -0.54%/a. The degree of glacier retreat in different periods varied. During 2000-2005, glacier area retreated by 15.53 km2, with an average rate of change of -0.60%/a. During 2005-2010, glacier area retreated by 31.80 km2, with an average rate of change of -1.26%/a, representing a sharp decline in glacier area. During 2010-2015, glacier area retreated by 0.63 km2, with an average rate of change of -0.03%/a. Glacier area retreated by 8.55 km2 from 2015 to 2020, with an average rate of change of -0.36%/a, which indicated a significant acceleration in the rate of glacier ablation that can be attributed to global warming. In terms of glacier volume, the glacier surface elevation changed significantly from 2000 to 2020, with the glacier volume decreasing by 23.17×10-3 km3 during this period. Glacier volume decreased the most during 2005-2010, with the value of 12.08×10-3 km3, and then the decreasing trend slowed down. Glacier volume was relatively stable during 2010-2015, with a decrease of 0.32×10-3 km3, followed by a decrease of 5.22×10-3 km3 during 2015-2020. The number of glaciers showed an increasing trend from 2010 to 2015, indicating that glacier fragmentation was more severe due to glacier ablation.
Table 2 Distribution and variation of glaciers in the Manas River Basin from 2000 to 2020
Year Glacier area
(km2)		 Glacier coverage
rate (%)		 Rate of change in glacier area (%) Change in glacier volume (×10−3 km3) Glacier
number		 Rate of change in glacier number (%)
2000 520.12 10.14 - - 747 -
2005 504.59 9.83 -2.99 -3.42 726 -2.81
2010 472.79 9.21 -6.30 -12.08 727 0.14
2015 472.16 9.20 -0.13 -0.32 767 5.50
2020 463.61 9.03 -1.81 -5.22 770 0.39

Note: "-" means no data.

The distribution of glacier area and number varied across different glacier scales (Fig. 5). Based on the size of the glacier area, we classified the glaciers into 4 scales: tiny glaciers (<1.00 km2), small-scale glaciers (1.00-5.00 km2), medium-scale glaciers (5.00-20.00 km2), and large-scale glaciers (>20.00 km2). Glaciers with area larger than 5.00 km2 were collectively referred to as larger glaciers in the study. Glaciers with area less than 5.00 km2 were collectively referred to as smaller glaciers. There were a large number of smaller glaciers covering relatively small area; however, there were fewer larger glaciers covering a relatively large area. This study found that tiny glaciers accounted for 33.50% of the total glacier area and 87.26% of the total glacier number on average for many years. During 2000-2020, small-scale glaciers accounted for 29.40% (multi-year average) of the total glacier area and 10.24% of the total glacier number; medium-scale glaciers accounted for 31.49% of the total glacier area and 2.36% of the total glacier number; and large-scale glaciers accounted for only 5.61% of the total glacier area, with only one such glacier. The retreat rates of glaciers at different scales were also very different from 2000 to 2020. Small-scale and medium-scale glaciers seriously retreated, with reduction rates of the glacier area of 8.36% and 14.46%, respectively. The retreat rate of large-scale glacier was 2.07%. In terms of the number of glaciers, tiny glaciers increased significantly. With the increase in glacier scale, the number of smaller glaciers decreased exponentially, but the number of larger glaciers remained relatively stable during 2000-2020. In addition, the glacier area and number of glaciers decreased rapidly before 2010. After 2010, glacier area decreased slowly, while the number of glaciers increased rapidly, and larger glaciers were transformed into smaller glaciers. In summary, the area of larger glaciers has greatly reduced due to the rising temperature. With the melting of the glacier, the glacier tongue area has shrunk significantly, and the whole glacier has been fragmented to varying degrees.
Fig. 5 Distribution and variation characteristics of glaciers at different scales in the Manas River Basin from 2000 to 2020

3.2 Distribution and variation characteristics of glacier area in the Manas River Basin

Glacier area in the Manas River Basin showed a normal distribution trend of first increasing and then decreasing with elevation (Fig. 6). At the elevation range of 3700-4800 m, the glacier area accounted for 97.92% of the total glacier area; however, little glacier area was distributed at elevations below 3800 m and above 4800 m. In accordance with the retreat rates of glaciers at different elevations, we divided the elevations into three zones. Among them, the first interval was below 3600 m. In this zone, the retreat rate of glaciers was the fastest from 2000 to 2020, with an average retreat rate of 68.82%. In the area with elevation below 3300 m, the glaciers retreated by 100.00% from 2015 to 2020. The second interval was 3600-4200 m, in which the retreat rate of glaciers slowed down, with an average rate of 27.84% in the past 21 a. The third interval was above 4200 m, and the average retreat rate of glaciers was about 6.67%, but it tended to accelerate at extremely high elevations (above 5200 m). The difference was that during 2015-2020, glaciers increased abnormally at the elevations of 3400-3600 m, which may be caused by the melting and downward movement of glaciers at high elevations. The results indicated that the retreat rate of glaciers decreased with increasing elevation under the context of global change, and rapid ablation or disappearance of glaciers occurred in the lower elevation zones, with a significant increase in elevation at the glacier terminus.
Fig. 6 Variation characteristics of glacier area with elevation in the Manas River Basin from 2000 to 2020
The glacier distribution according to slope directions remained the same over the past 21 a, with a larger proportion of glaciers (averaging about 48.11%) found on the northern and northeastern slopes and the smallest proportion of glaciers (averaging about 14.41%) observed on the western and southwestern slopes (Fig. 7a). Glaciers at different slope directions showed different retreat rates (Fig. 7b). Specifically, glaciers on the western and southwestern slopes retreated sharply from 2000 to 2010, while glaciers on the eastern and southeastern slopes retreated slowly from 2000 to 2005 but increased rapidly from 2005 to 2010. From 2010 to 2015, the retreat rate of glaciers on the southern slope increased, while the glacier area on the northern slope showed a slight increasing trend, so it can be inferred that glaciers were thinning or moving slightly northwards during this period. During 2015-2020, the retreat rate of glaciers became larger on the northern and northeastern slopes, where glaciers were distributed on larger scales, while the retreat rate showed a decreased level of change or even a weak increasing trend on the southern and southeastern slopes. To sum up, from 2000 to 2020, glaciers at different slope directions showed a rapid melting trend from the western slope to the southern slope then to the northern slope. This may be related to the marked difference in terrestrial radiation obtained at different slope directions, which directly affects the changes in glacier area or retreat rate.
Fig. 7 Variation characteristics of glacier area (a) and retreat rate (b) according to slope directions in the Manas River Basin from 2000 to 2020. N, north; NNE, north-northeast; NE, northeast; ENE, east-northeast; E, east; ESE, east-southeast; SE, southeast; SSE, south-southeast; S, south; SSW, south-southwest; SW, southwest; WSW, west-southwest; W, west; WNW, west-northwest; NW, northwest; NNW, north-northwest.

3.3 Temporal variation of glacial runoff and its contribution rates to river runoff

Glacial runoff is a very precious water resource in arid inland river basins and plays an important role in regulating the recharge of river runoff. In the past 21 a, glacial runoff in the basin showed a fluctuating and changing state, with an average annual runoff of 4.80×108 m3 and an average increase rate of 0.03×108 m3/a (Fig. 8). The minimum glacial runoff (3.26×108 m3) appeared in 2019, and the maximum glacial runoff (6.97×108 m3) appeared in 2011. Fluctuations in glacial runoff during different periods indirectly resulted in changes in the abundance and depletion of river runoff.
Fig. 8 Temporal variations of glacial runoff and its contribution rates to river runoff in the Manas River Basin from 2000 to 2020. Due to limitations in the observed river runoff data, annual contribution rate data were only available up to 2016.
Warming climatic environments accelerated glacier melting, and the increased contribution of glacial meltwater to river runoff indicated a continued increase in the recharge of river runoff from glacial meltwater. Due to limitations in the recorded data, the observed river runoff was only available up to 2016. From 2000 to 2016, the observed average annual river runoff in the basin was 13.88×108 m3, and the average annual glacial runoff was 4.91×108 m3, of which 33.31% was from the ablation season (June-September). The average annual contribution rate of glacial meltwater to river runoff was 35.40% (Fig. 8), and glacial meltwater accounted for 45.37% of the total runoff during the ablation season.
Figure 9 presents the complementary regulation of precipitation and glacial runoff on river runoff. Glacial runoff was the main component of river runoff, and its proportion was increasing. The fluctuation in the contribution of glacial meltwater to river runoff reflects the fact that its recharge to river runoff is not only affected by the intensity of glacier melting but is also related to precipitation and other factors. Through the rate of change of glacial runoff and precipitation in the basin, it was found that river runoff in the basin was highly correlated with precipitation from 2000 to 2003. From 2003 to 2011, river runoff was significantly influenced by the synergistic effects of glacial runoff and precipitation. This reflects the synchronous seasonal patterns of glacial runoff and precipitation, both of which peak in summer, and that the climate in the basin is characterized by simultaneous rain and heat. After 2011, river Runoff followed the same trend as precipitation and the opposite trend of glacial runoff.
Fig. 9 Relationship between rates of change in glacial runoff, precipitation, and observed river runoff in the Manas River Basin from 2000 to 2020

3.4 Threshold analysis of factors influencing the glacier ablation

Quantitative analysis of climate change in the glacier disappearance regions showed that the cycle of temperature change averaged about 4 a in the glacier disappearance regions over the past 20 a. The average summer temperature (June-August) during the period of glacier disappearance was 0.98°C. The highest melting point in the glacier area was in the eastern region (averaging 1.37°C), where glacier ablation was the slowest, followed by the glacier area in the western region (averaging 0.81°C) (Fig. 10). The lowest melting point was in the central region (averaging 0.74°C), where glacier ablation was the fastest. Average summer precipitation in the glacier disappearance regions reached 305.2 mm, with the maximum precipitation occurring in 2003 and 2016, reaching more than 420.0 mm.
Fig. 10 Temporal variations of summer average temperature and summer precipitation in the glacier disappearance regions of the Manas River Basin from 2000 to 2020. Eastern region, central region, and western region were three subdivisions of glacier disappearance regions.

4 Discussion

4.1 Reliability analysis and influence factor analysis of glacial runoff

Based on the glacier boundaries extracted in this study and the distributed degree-day model combined with data on the year-to-year glacier area, we found that the total number of glaciers in the Manas River Basin decreased and glacial runoff increased from 2000 to 2020. The glacial runoff simulation methodology adopted in this study has been widely used by many scholars (Cao et al., 2019; Zhao et al., 2020; Wang et al., 2023; Ni et al., 2024). In the 1980s, Yang (1987) estimated that the meltwater recharge in the Manas River Basin could reach 34.60%, which is consistent with the results of the present study. Zhao et al. (2020) concluded that the contribution rate of glacial meltwater in the Manas River Basin was 25.00%, which is lower than that in the present study. Chen et al. (2019) found that glacial runoff accounted for 42.00% of the river runoff in the Manas River Basin during the ablation season, which is similar to the result in the current study. According to the research by Sun et al. (2016), the contribution rate of glacial meltwater to river runoff was 27.10% across the northern Tianshan Mountains, with the runoff recharge varying from the west to the east. Zhao et al. (2020) concluded that glacial runoff of the Manas River Basin in July and August accounted for 55.10% of the annual glacial runoff. It can be inferred that the difference in glacial runoff simulation may be related to differences in data sources and glacier areas. Moreover, taking into consideration the relationship between glacier area and volume proposed by Liu et al. (2003), the results showed that the glacier reserve in the Manas River Basin decreased by 26.71 km3 from 2000 to 2020, which can be converted to a water equivalent of 22.70×10-3 km3 (assuming an average glacier density of 850 kg/m3), and ice-melting amount simulated by the model was 23.17×10-3 km3 in the same period. In summary, the simulation results of this study are very close to those of other studies, indicating that the method in the current study can better simulate the glacial runoff in the basin with high reliability.
In addition, this study found that the contribution rate of glacial meltwater to river runoff was unusually high in 2011. This may be due to higher summer temperature in 2011 (0.61℃ higher than that in 2010), which led to more glacier ablation, while river runoff was smaller in that year. In addition, Peng et al. (2022) showed that there was significant warming and humidification in the Tianshan Mountains after 2009. The increase in temperature and precipitation led to an increase in the contribution of precipitation and glacial meltwater to river runoff in the Manas River Basin. Besides temperature and precipitation, the geographical location of glaciers and differences in topography can lead to different sources of solar radiation and water vapor being received by glaciers, which will further affect the glacier changes (Ren, 2004). This study found that the average elevation of the glacier disappearance regions was about 4075 m, and 20.30% of the glacier disappearance regions was distributed at 4300-4600 m and about 79.70% of the glacier disappearance regions was located below 4300 m. The reason why glaciers are less distributed at lower elevations is that the climate in this regions is dry and warm, and there is a lower water vapor supply, which cannot be conducive to the development and accumulation of glaciers. Under the influence of local circulation, there is a "second largest precipitation zone" in the glacier area (Benn et al., 2012). Therefore, the accumulation of glaciers above 5000 m also depends on the supply of the second largest precipitation zone to some extent. The average slope of glacier disappearing regions was about 38°, which is consistent with the finding of Ji et al. (2023) that glacier subsidence is most severe and deformable in the slope range of 40°-45°. In addition, the glacier scale of glacier disappearance regions was mainly less than 0.10 km2, and the fragmentation of glaciers at the scale of 0.10-2.00 km2 was more serious, indicating a more serious disappearance of small-scale glaciers. All these factors had an important influence on glacier melting. Most glacial meltwater modeling studies have used empirical formulas and snow parameters to adjust the glacier module (Silwal et al., 2023). A lack of a debugging basis for glacier simulation parameters greatly increases the uncertainty of glacier research. The threshold analysis in this study can provide a scientific reference for model construction and parameter adjustment.
To sum up, the findings of this study can provide scientific support for the construction of hydrological data and the rational utilization of water resources in the Manas River Basin and other similar arid inland river basins.

4.2 Limitations and prospects

Within the context of climate warming, glacier melting has intensified, and glacial runoff is an important source of water that cannot be ignored in arid areas. Domestic and international research on glacial runoff mainly focuses on the snowmelt model, while there has been less attention to the formation mechanisms of glacial runoff. It is increasingly recognized that the complexity of the physical and hydrological processes involved in the formation of glacial runoff adds uncertainty to the evaluation of glacial runoff. The uncertainty of the model input parameters and the lack of station observation data pose great challenges to glacial runoff simulation studies (La Frenierre and Mark, 2014). One of the most important sources of uncertainty in glacial runoff modeling is parameter selection, with the chosen parameters usually selected from the available good model parameters; this is a highly subjective process and can increase the uncertainty of simulation results to some extent (Dolk et al., 2020).
This study utilized the distributed degree-day model combined with data on the year-to-year glacier area to simulate glacial runoff. This approach differs from that of Yang and Bai (2024) using the distributed Hydrologska Byråns Vattenbalansavdelning (HBV) hydrological model. The reasons may be closely related to model selection, data input, and parameter correction. Although the simulation results have certain applicability, other effects such as solar radiation, atmospheric pressure, and wind speed on glacier ablation have not been considered in detail in the process of model construction. These factors have a certain influence on the output of the results, and still require further in-depth study.

5 Conclusions

This study analyzed the temporal variation characteristics in glacier area and glacial runoff in the Manas River Basin from 2000 to 2020, and explored the climatic and environmental factors that caused glacier ablation. The main conclusions are as follows:
(1) The average coverage rate of glacier area in the Manas River Basin was about 9.48% over the past 21 a, with an overall downward trend. From 2000 to 2020, glacier area decreased by 10.86%. Smaller glaciers were more numerous, covered a smaller area, and were seriously shrinking, while larger glaciers were less in number but relatively stable in area.
(2) Glacier area in the basin showed a normal distribution trend of first increasing and then decreasing with elevation. About 97.92% of glaciers were distributed at 3700-4800 m. The retreat rate of glaciers was the fastest in the regions below 3600 m, and the elevation of the end of the glaciers rose significantly. More glaciers were found on the northern and northeastern slopes, and fewer glaciers were observed on the western and southwestern slopes. Minor differences in glacier changes were found in different periods on various slope directions, and glaciers at different slope directions showed a rapid melting trend from the western slope to the southern slope then to the northern slope.
(3) In the past 21 a, glacial runoff fluctuated in the basin, with an average annual runoff of 4.80×108 m3. The minimum glacial runoff occurred in 2019 and the maximum glacial runoff occurred in 2011. Over the past 21 a, glacier ablation accelerated, and the contribution of glacial meltwater to river runoff showed a fluctuating upward trend, with a sustained increase in runoff recharge.
In this study, glacial runoff in the basin was studied by means of a distributed day-degree model. The model is simple and convenient to operate and has a wide range of applications, but it ignores many other factors, such as solar radiation, albedo, human activities, and so on. In addition, continued warming will lead to sudden flooding caused by melting snow and ice, which will bring some uncertainty to the prediction of seasonal runoff, and will seriously affect the sustainable utilization of regional water resources and human production and life. In a follow-up study, we should focus on in-depth research of the above scientific issues, effectively providing a scientific basis for the planning and management of water resources in the Manas River Basin.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported by the National Natural Science Foundation of China (52169005), the Support Plan for Innovation and Development of Key Industries in southern Xinjiang, China (2022DB024), and the Corps Science and Technology Innovation Talents Program Project of China (2023CB008-08).

Author contributions

Conceptualization: WANG Tongxia, ZHANG Zhengyong; Methodology: WANG Tongxia, HE Chaofei; Formal analysis: WANG Tongxia, CHEN Fulong, LIU Bo; Writing - original draft preparation: WANG Tongxia, CHEN Fulong; Writing - review and editing: WANG Tongxia, CHEN Fulong, ZHANG Zhengyong; Funding acquisition: CHEN Fulong; Resources: CHEN Fulong, LYU Tingbo, HUANG Yanhao; Supervision: CHEN Fulong, LONG Aihua, ZHANG Zhengyong. All authors approved the manuscript.

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
Benn D I, Bolch T, Hands K, et al. 2012. Response of debris-covered glaciers in the Mount Everest region to recent warming, and implications for outburst flood hazards. Earth-Science Reviews, 114(1-2): 156-174.

[2]
Bliss A, Hock R, Radić V. 2014. Global response of glacier runoff to twenty-first-century climate change. Journal of Geophysical Research: Earth Surface, 119(4): 717-730.

[3]
Cao B, Pan B T, Wen Z L, et al. 2019. Changes in glacier mass in the Lenglongling Mountains from 1972 to 2016 based on remote sensing data and modeling. Journal of Hydrology, 578: 124010, doi: 10.1016/j.jhydrol.2019.124010.

[4]
Chen H Y, Chen Y N, Li W H, et al. 2019. Quantifying the contributions of snow/glacier meltwater to river runoff in the Tianshan Mountains, Central Asia. Global and Planetary Change, 174: 47-57.

[5]
Ding Y J, Liu S Y, Li J, et al. 2006. The retreat of glaciers in response to recent climate warming in western China. Annals of Glaciology, 43: 97-105.

[6]
Dolk M, Penton D J, Ahmad M D. 2020. Amplification of hydrological model uncertainties in projected climate simulations of the Upper Indus Basin: Does it matter where the water is coming from? Hydrological Processes, 34(10): 2200-2218.

[7]
Donovan M, Belmont P, Notebaert B, et al. 2019. Accounting for uncertainty in remotely sensed measurements of river planform change. Earth-Science Reviews, 193: 220-236.

[8]
Du W B, Ji W Q, Xu L J, et al. 2020. Deformation time series and driving-force analysis of glaciers in the eastern Tienshan Mountains using the SBAS InSAR Method. International Journal of Environmental Research and Public Health, 17(8): 2836, doi: 10.3390/ijerph17082836.

[9]
Gao X, Zhang S Q, Ye B S, et al. 2010. Glacier runoff change in the upper stream of Yarkant River and its impact on river runoff during 1961-2006. Journal of Glaciology and Geocryology, 32(3): 445-453. (in Chinese)

[10]
Guo W Q, Liu S Y, Xu J L, et al. 2015. The second Chinese glacier inventory: Data, methods and results. Journal of Glaciology, 61(226): 357-372.

[11]
He C F, Chen F L, Long A H, et al. 2023. Improving the precision of monthly runoff prediction using the combined non-stationary methods in an oasis irrigation area. Agricultural Water Management, 279: 108161, doi: 10.1016/j.agwat.2023.108161.

[12]
Hirose J M R, Marshall S J. 2013. Glacier meltwater contributions and glaciometeorological regime of the Illecillewaet Rriver Basin, British Columbia, Canada. Atmosphere-Ocean, 51(4): 416-435.

[13]
Huang L, Li Z, Zhou J M, et al. 2021. An automatic method for clean glacier and nonseasonal snow area change estimation in High Mountain Asia from 1990 to 2018. Remote Sensing of Environment, 258: 112376, doi: 10.1016/j.rse.2021.112376.

[14]
Hugonnet R, McNabb R, Berthier E, et al. 2021. Accelerated global glacier mass loss in the early twenty-first century. Nature, 592(7856): 726-731.

[15]
Huss M, Hock R. 2018. Global-scale hydrological response to future glacier mass loss. Nature Climate Change, 8(2): 135-140.

[16]
Janke J R. 2013. Using airborne LiDAR and USGS DEM data for assessing rock glaciers and glaciers. Geomorphology, 195: 118-130.

[17]
Ji Q, Zhang C L, Ding Y K, et al. 2023. Glacier monitoring in Qomolangma Nature Reserve based on multi-source remote sensing data. Arid Land Geography, 46(10): 1591-1601. (in Chinese)

[18]
Kumar A, Tiwari S K, Verma A, et al. 2018. Tracing isotopic signatures (δD and δ18O) in precipitation and glacier melt over Chorabari Glacier-Hydroclimatic inferences for the Upper Ganga Basin (UGB), Garhwal Himalaya. Journal of Hydrology: Regional Studies, 15: 68-89.

[19]
La Frenierre J L, Mark B G. 2014. A review of methods for estimating the contribution of glacial meltwater to total watershed discharge. Progress in Physical Geography: Earth and Environment, 38(2): 173-200.

[20]
Li K M, Li H L, Wang L, et al. 2011. On the relationship between local topography and small glacier change under climatic warming on Mt. Bogda, Eastern Tian Shan, China. Journal of Earth Science, 22(4): 515-527.

[21]
Li M, Xiao P F, Zhang X L, et al. 2022. An improved approach of dry snow density estimation using C-band synthetic aperture radar data. ISPRS Journal of Photogrammetry and Remote Sensing, 191: 49-67.

[22]
Liu H M, Chen W M, Dong X B, et al. 2009. Sustainable agricultural paradigm of mountain-oasis-ecotone-desert system in inland Manasi River Basin, Xinjiang, northwest China. In: Li D, Zhao C J. Computer and Computing Technologies in Agriculture II, Volume 1. CCTA 2008. IFIP Advances in Information and Communication Technology (Vol. 293). Boston: Springer.

[23]
Liu S Y, Ding Y J, Wang N L, et al. 1998. Mass balance sensitivity to climate change of the Glacier No.1 at the Urumqi River Head Tianshan Mts. Journal of Glaciology and Geocryology, 20(1): 9-13. (in Chinese)

[24]
Liu S, Sun W X, Shen Y P, et al. 2003. Glacier changes since the Little Ice Age maximum in the western Qilian Shan, northwest China, and consequences of glacier runoff for water supply. Journal of Glaciology, 49(164): 117-124.

[25]
Luo Y. 2012. The mass balances modeling and the relationship with meteorological parameters on glaciers in Tianshan, Xinjiang, China. MSc Thesis. Lanzhou: Northwest Normal University. (in Chinese)

[26]
Miles K E, Hubbard B, Irvine-Fynn T D L, et al. 2020. Hydrology of debris-covered glaciers in High Mountain Asia. Earth-Science Reviews, 207: 103212, doi: 10.1016/j.earscirev.2020.103212.

[27]
Ni M X, Muhtar P, Sun T Y, et al. 2024. Analysis on the supply-demand relationship of water resources based on the dual role of climate change and human activities in southern Xinjiang, China. Journal of Basic Science and Engineering, 32(2): 331-348. (in Chinese)

[28]
Paul F, Bolch T, Briggs K, et al. 2017. Error sources and guidelines for quality assessment of glacier area, elevation change, and velocity products derived from satellite data in the Glaciers_cci project. Remote Sensing of Environment, 203: 256-275.

[29]
Peng Z T, Zhang Y D, Zhu L J, et al. 2022. Divergent tree growth and the response to climate warming and humidification in the Tianshan Mountains, China. Forests, 13(6): 886, doi: 10.3390/f13060886.

[30]
Rafiq M, Romshoo S A, Mishra A K, et al. 2019. Modeling Chorabari Lake outburst flood, Kedarnath, India. Journal of Mountain Science, 16(1): 64-76.

[31]
Ren J W, Qin D H, Kang S C, et al. 2004. Glacier variations and climate warming and drying in the central Himalayas. Chinese Science Bulletin, 49(1): 65-69.

[32]
Shi S X, Xing L, Ding Y K, et al. 2022. Characteristics and changes of the Himalayas glacial area in China during 1990-2015. Journal of Mountain Science, 19(7): 1961-1973.

[33]
Silwal G, Ammar M E, Thapa A, et al. 2023. Response of glacier modeling parameters to time, space, and model complexity: Examples from eastern slopes of Canadian Rocky Mountains. Science of the Total Environment, 872: 162156, doi: 10.1016/j.scitotenv.2023.162156.

[34]
Singh D K, Thakur P K, Naithani B P, et al. 2021. Quantifying the sensitivity of band ratio methods for clean glacier ice mapping. Spatial Information Research, 29(3): 281-295.

[35]
Sorg A, Bolch T, Stoffel M, et al. 2012. Climate change impacts on glaciers and runoff in Tien Shan (Central Asia). Nature Climate Change, 2(10): 725-731.

[36]
Su B, Xiao C D, Chen D L, et al. 2022. Glacier change in China over past decades: Spatiotemporal patterns and influencing factors. Earth-Science Reviews, 226: 103926, doi: 10.1016/j.earscirev.2022.103926.

[37]
Sun C J, Yang J, Chen Y N, et al. 2016. Comparative study of streamflow components in two inland rivers in the Tianshan Mountains, Northwest China. Environmental Earth Sciences, 75(9): 727, doi: 10.1007/s12665-016-5314-1.

[38]
Sun M P, Liu S Y, Yao X J, et al. 2018. Glacier changes in the Qilian Mountains in the past half-century: Based on the revised First and Second Chinese Glacier Inventory. Journal of Geographical Sciences, 28(2): 206-220.

[39]
Tang H. 2023. Simulation of Manas River runoff based on reanalysis dataset. MSc Thesis. Shihezi: Shihezi University. (in Chinese)

[40]
Tielidze L G, Nosenko G A, Khromova T E, et al. 2022. Strong acceleration of glacier area loss in the Greater Caucasus between 2000 and 2020. The Cryosphere, 16(2): 489-504.

[41]
Unger-Shayesteh K, Vorogushyn S, Farinotti D, et al. 2013. What do we know about past changes in the water cycle of Central Asian headwaters? A review. Global and Planetary Change, 110: 4-25.

[42]
Wang P Y, Li H L, Li Z Q, et al. 2024. Rapid mass losses of Urumqi River Basin glaciers, eastern Tianshan Mountains revealed from multi-temporal DEMs, 1964-2021. International Journal of Digital Earth, 17(1): 2295990, doi: 10.1080/17538947.2023.2295990.

[43]
Wang R J, Ding Y J, Shangguan D H, et al. 2023. Projections of glacier peak water and its timing in the Sanjiangyuan on the Tibetan Plateau. Journal of Hydrology: Regional Studies, 45(4): 101313, doi: 10.1016/j.ejrh.2022.101313.

[44]
Wang T X, Zhang Z Y, Liu L, et al. 2021. Simulation of the potential distribution of the glacier based on maximum entropy model in the Tianshan Mountains, China. Water, 13(11): 1541, doi: 10.3390/w13111541.

[45]
Xu B R, Lu Z X, Liu S Y, et al. 2015. Glacier changes and their impacts on the discharge in the past half-century in Tekes watershed, Central Asia. Physics and Chemistry of the Earth, Parts A/B/C, 89-90: 96-103.

[46]
Yang Z L, Bai P. 2024. Response of runoff and its components to climate change in the Manas River of the Tian Shan Mountains. Advances in Climate Change Research, 15(1): 62-74.

[47]
Yang Z N. 1987. Glacier water resources in China. Resources Science, 9(1): 46-55. (in Chinese)

[48]
Yao G H, Zhou X B, Ke C Q, et al. 2022. The potential of Sentinel-1A data for identification of debris-covered alpine glacier based on machine learning approach. Remote Sensing, 14(9): 1980, doi: 10.3390/rs14091980.

[49]
Yao T D, Wang Y Q, Liu S Y, et al. 2004. Recent glacial retreat in High Asia in China and its impact on water resource in Northwest China. Science in China Series D: Earth Sciences, 47(12): 1065-1075.

[50]
Zhang H, Li Z Q, Zhou P, et al. 2018. Mass-balance observations and reconstruction for Haxilegen Glacier No.51, eastern Tien Shan, from 1999 to 2015. Journal of Glaciology, 64(247): 689-699.

[51]
Zhang S Q, Ye B S, Liu S Y, et al. 2012. A modified monthly degree-day model for evaluating glacier runoff changes in China. Part I: model development. Hydrological Processes, 26(11): 1686-1696.

[52]
Zhang Y, Liu S Y, Ding Y J. 2006. Spatial variation of degree-day factors on the observed glaciers in western China. Acta Geographica Sinica, 61(1): 89-98. (in Chinese)

[53]
Zhang Y, Liu S Y, Wang X. 2019a. A dataset of the spatial distribution of degree-day factors for glaciers in High Mountain Asia. China Scientific Data, 4(3): 141-151.

[54]
Zhang Z Y, Liu L, He X L, et al. 2019b. Evaluation on glaciers ecological services value in the Tianshan Mountains, Northwest China. Journal of Geographical Sciences, 29(1): 101-114.

[55]
Zhao G N, Zhang Z Y, Liu L, et al. 2020. Simulation and construction of the glacier mass balance in the Manas River Basin, Tianshan, China from 2000 to 2016. Journal of Geographical Sciences, 30(6): 988-1004.

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