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Warming in mountainous regions has experienced obvious elevation dependence (the warming rate of air temperature is amplified with elevation), which accelerates the melting of ice and snow, affecting glacier size and mass, and water resources in mountainous regions. Here, we used ERA5-Land monthly averaged datasets from 1979 to 2019 to analyze the elevation-dependent warming (EDW) variability, driving factor, and its implications to water resources in the Qilian Mountains (QLM). Results showed that: (1) Annual mean temperature shows a significant increasing trend in the QLM from 1979 to 2019 (0.31 °C/10a; p < 0.01), and the warming rate of annual mean temperature increased with elevation in the QLM in general. For seasonal scales, the most obvious EDW was found in summer. In the past 41 years, snow cover and snow depth showed a slight decreasing trend. There was a significant negative correlation between temperature and snow cover, which can explain that enhanced regional warming has caused decreases in snow cover and snow depth, largely controlling the pattern of EDW on the QLM. EDW has significant implications for water resources over QLM, especially above 5,000 m. Our study can provide a reference in understanding the importance of EDW to water resources in mountainous areas.

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  • ERA5-Land reanalysis 2 m temperature data are reliable in Qilian Mountains.

  • There is an obvious elevation-dependent warming (EDW) of annual mean temperature, especially above 5,000 m.

  • Snow cover in Qilian Mountain showed a decreasing trend.

  • Snow cover is an important mechanism driving EDW.

  • EDW has an important implication for water resources (especially above 5,000 m).

elevation-dependent warming, ERA5-Land reanalysis, Qilian Mountains, snow cover, water sources
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Mountainous areas, such as Qinghai-Tibet Plateau (QTP), Tianshan, have unique environments with complex terrain conditions and fragile ecological environment (Yi et al. 2018; Pepin et al. 2019). Climate change affects the accumulation and melting of glaciers and snow in mountains areas (Sun & Liu 2013; Gao et al. 2018a, 2020; Fan et al. 2021; Jiao et al. 2021; Zuo et al. 2021). Rapid warming is expected to accelerate the melting of ice and snow, affecting glacier size and mass, and water resources (Guo et al. 2021; Shen et al. 2021). Elevation-dependent warming (EDW) occurs when the rate of warming increases with elevation (Pepin et al. 2015; You et al. 2020; Miller et al. 2021). Positive EDW accelerates soil moisture in mountain ecosystems, warming of the cryosphere, intensification of hydrological processes, and evolution of Phenological (Pepin et al. 2015; Río et al. 2021). Enhanced warming at high altitudes must be a considered for future climate adaptation strategies in sensitive mountainous regions (Johnston et al. 2018). Evidence for EDW has been obtained mainly from other high mountain ranges, such as the Swiss Alps (Rottler et al. 2019), Colorado Rocky Mountains (Minder et al. 2018), Tropical Andes (Aguilar-Lome et al. 2019; Toledo et al. 2021), and the Tibetan Plateau (Niu et al. 2021a, 2021b; Shen et al. 2021). The extent of EDW remains uncertain despite extensive research.

Many previous studies on EDW were based on observations and model simulations (Tudoroiu et al. 2016; Thakuri et al. 2019; Li et al. 2020; You et al. 2020). However, due to the scarcity of ground observation data, especially at elevations above 3,000 m, research on EDW in mountainous areas is limited (Rangwala & Miller 2012). A more comprehensive ground observation network is urgently needed, as well as reanalysis data, remote sensing data, and high-resolution climate modeling with better representation of both atmospheric and cryosphere processes to gain a greater understanding of EDW (Guo et al. 2019; You et al. 2020; Niu et al. 2021a). Palazzi et al. (2018) used Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations to assess EDW in the QTP, and results found that the warming rate in high-altitude areas has accelerated (Palazzi et al. 2018). Zhang et al. (2020) showed that air temperature increased at elevation ranges of 2,000–3,000 and 4,000–5,000 m, and cooled at 6,000–7,000 m. Gao et al. (2018b) used meteorological observations, ERA-interim reanalysis, and the Weather Research and Forecasting Mode (WRF) for the QTP to analyze EDW above 5,000 m, results showed that ERA-interim underestimated the increasing rate of temperature, and there was no obvious EDW above 3,000 m. Although satellite data can compensate for the lack of ground observation stations, the relatively short-time series, and long revisiting cycle limit the understanding of EDW variability. A high-resolution and long-time series air temperature dataset is necessary for more accurate EDW detection (Pepin et al. 2015).

The Qilian Mountains are an important ecological protection barrier and one of the most important sources of water in northwestern China. The mountain range is extremely important for assessing climatic and environmental changes across China (Lin et al. 2017; Wang et al. 2019). The QLM system is not only the source of many rivers but also hosts a unique desert oasis ecosystem (Sun & Liu 2013; Wang et al. 2019). However, most glaciers in the QLM exhibit an accelerated degradation due to recent climate warming (Qian et al. 2019). The warming rate of mean annual temperature in the entire QLM reached 0.33 °C/10a during 1960–2016, which is higher than the respective national mean (0.23 °C/10a) (Wang et al. 2019; Wen et al. 2019). However, the implications of EDW on water resources specialized in the QLM are unclear, perhaps due to the spare meteorological station, especially above 3,500 m. An evaluation of EDW in the QLM is critical for increased accuracy of estimates of the mass balance (and associated runoff) of high-altitude ice masses, and for understanding changes in the alpine environment of the QLM (Pepin et al. 2015).

Here, we analyzed the EDW variability, physical mechanism, and its implications for water resources based on the newest ERA5-Land reanalysis temperature datasets from 1979 to 2019 in the QLM. Section 2 describes the data and methods. Section 3 focuses on the EDW variability, driving factors, and implications of EDW to water resources. Section 4 concludes this study.

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Data

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ERA5-Land monthly averaged data were derived from the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5-Land is a state-of-the-art global reanalysis dataset for land applications, which has a higher resolution than ERA-Interim and ERA5 reanalysis data. The spatial resolution and horizontal resolution of ERA5-Land are 0.1° × 0.1° and 10 km, respectively. The ERA5-Land dataset includes hourly and monthly dynamic data representing 50 indicators from 1950 to present (Alexander & Gregor 2020; Cao et al. 2020; Jiang et al. 2020; Luis & Johannes 2020; Pelosi et al. 2020; Zandler et al. 2020; Joaquín et al. 2021; Konstantinos et al. 2021; Pelosi & Chirico 2021; Wu et al. 2021; Xu et al. 2022). In this study, the period of ERA5-Land ranged from January 1979 to December 2019, while the geographical locations ranged from 35.8 to 40.0°N and from 93.5 to 104.0°E, which can cover all the QLM region (Figure 1). 2 m temperature data, snow cover data, snow depth data, and snow depth water equivalent data over the QLM were used in this study. The monthly temperature data of 17 meteorological stations in the QLM were compared with the ERA5-Land grid data corresponding to the station to evaluate the reliability of ERA5-Land 2 m temperature data. The results show that there is an underestimation of the ERA5-Land grid data in the temperature range less than 0 °C. In general, the determination coefficient of R2 = 0.99 indicates that ERA5-Land reanalysis temperature data has a good applicability in the QLM (Figure 2), which can be used to analyze the temperature change characteristics in the QLM. Although there are few observational stations in the Qilian Mountains, especially above 3,500 m, many studies have indicated that limited stations can also evaluate the applicability of reanalysis data effectively and accurately. For example, Zhao & He (2022) found that ERA5-Land reanalysis temperature data can generally capture the temperature trend of observations very well and is reliable for scientific research over the Qilian Mountains. In addition, digital elevation model (DEM) data in the QLM were downloaded from the Geospatial Data Cloud (https://www.gscloud.cn/). Four seasons are defined as spring (March to May), summer (June to August), autumn (September to November), and winter (December to the following February).
Figure 1
Spatial distribution of ERA5-Land grid points within the QLM.
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Spatial distribution of ERA5-Land grid points within the QLM.

Figure 1
Spatial distribution of ERA5-Land grid points within the QLM.
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Spatial distribution of ERA5-Land grid points within the QLM.

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Figure 2
Scatter plot of the comparison between observations and ERA5-Land monthly mean temperature in the QLM, with the black dashed line indicating that the temperature is equal to 0 °C.
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Scatter plot of the comparison between observations and ERA5-Land monthly mean temperature in the QLM, with the black dashed line indicating that the temperature is equal to 0 °C.

Figure 2
Scatter plot of the comparison between observations and ERA5-Land monthly mean temperature in the QLM, with the black dashed line indicating that the temperature is equal to 0 °C.
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Scatter plot of the comparison between observations and ERA5-Land monthly mean temperature in the QLM, with the black dashed line indicating that the temperature is equal to 0 °C.

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Methods

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Altitudes in the QLM were divided into seven ranges: 2,000 − 2,500 m, 2,500 − 3,000 m, 3,000 − 3,500 m, 3,500 − 4,000 m, 4,000 − 4,500 m, 4,500 − 5,000 m, and 5,000 − 5,500 m, respectively (Table 1). There are a total of 1983 grid points within the QLM, and the percentage of grid points in each altitude range to the total grid points is also shown in Table 1. The divided justification with 500 m interval was according to the literature (Williamson et al. 2020; Gao et al. 2021). Mann-Kendall (MK) trend test and Sen's slope approach were used to analyze the temperature trends (Tayfur & Yacoub 2019). Correlation analysis was used to analyze the relationship between temperature and snow cover as well as snow depth (Jiang et al. 2016).

Table 1

ERA5-Land grid number and percentage for each altitude range over the QLM

Elevation range (m)Grid numberPercentage (%)
2,000 − 2,500 8 0.40 
2,500 − 3,000 229 11.55 
3,000 − 3,500 583 29.40 
3,500 − 4,000 569 28.69 
4,000 − 4,500 471 23.75 
4,500 − 5,000 105 5.30 
5,000 − 5,500 17 0.86 
Elevation range (m)Grid numberPercentage (%)
2,000 − 2,500 8 0.40 
2,500 − 3,000 229 11.55 
3,000 − 3,500 583 29.40 
3,500 − 4,000 569 28.69 
4,000 − 4,500 471 23.75 
4,500 − 5,000 105 5.30 
5,000 − 5,500 17 0.86 
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Temperature trend

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Against the background of global warming, the annual mean temperature in the QLM shows an increasing trend in general. Table 2 reveals that the annual mean temperature shows an increasing trend with a rate of 0.31 °C/10a (p < 0.01). The increasing trends of seasonal mean temperature followed the order of spring > summer > autumn. There is no significant increasing trend in winter. EDW variabilities are shown in Figures 3 and 4. EDW could be detected for annual mean temperature, in which 5,000–5,500 m is the elevation range with the largest warming rate, reaching 0.38 °C/10a. EDW could also be detected in spring with the greatest warming rate found in 5,000–5,500 m (Figure 4). In summer, the rate of warming increases with elevation, reaching a maximum of 0.46 °C/10a at 4,500–5,000 m before slowing down. In autumn, the rate of warming is significantly elevation-dependent, rising from 0.29 °C/10a at 2,000–2,500 m to 0.32 °C/10a at 2,500–3,000 m, reaching a maximum of 0.46 °C/10a at 5,000–5,500 m. There is no significant EDW in winter. We also analyzed the temperature trends of annual mean temperature, spring temperature and autumn temperature at different periods (Figures 5,6–7). Warming rates of annual mean temperature showed a similar result with the temperature trend calculated directly from 1979 to 2019. The biggest warming rates (0.63 °C/10a, p < 0.01) were found at the elevation range of 5,000–5,500 m in 1980–2009 and 1981–2010 (Figure 5). Spring and autumn mean temperature also showed similar results with the trends calculated directly from 1979 to 2019. Spring mean temperature shows the biggest warming rate of 0.70 °C/10a (p < 0.01) in 1981–2010 and 1988–2017 (Figure 6). Autumn average temperature shows the biggest warming rate of 0.93 °C/10a (p < 0.01) in 1979–2008 (Figure 7). Based on the ERA-Interim reanalysis data, Gao et al. (2018b) concluded that EDW in 5,000–5,500 m over the QTP is not occurring, which is contrary to the results of this study. The reason may be that ERA-Interim has a coarser resolution compared with the ERA5-Land data. However, Zhang et al. (2022) found that EDW is only valid up to about 5,000 m on the QTP when using high-resolution reanalysis air temperature datasets. The reason why the results of Gao et al. (2018b) and Zhang et al. (2022) are different is that the resolution of these two datasets is different. In addition, Zhang et al. (2022) used the dataset based on moderate resolution imaging spectroradiometer land surface temperature (MODIS LST) data, which is greatly affected by the cloud. Finally, the driving data and algorithms of the two data products are different, which leads to the difference in accuracy of the two products, and then affects the EDW results. Li et al. (2020) analyzed the EDW characteristics of high mountain Asia (HMA) by using observational temperature data and found that there was an obvious EDW phenomenon in the HMA area at an altitude of 2,500–5,000 m during 1980–2017, which is similar to this study.
Table 2

Temperature trends from 1979 to 2019 at different time scales in the QLM

Temperature (°C)MK (Z)Trendβ (°C/10a)
Annual 4.62 Yes (+) 0.31** 
Spring 3.74 Yes (+) 0.43** 
Summer 3.96 Yes (+) 0.42** 
Autumn 2.73 Yes (+) 0.25** 
Winter 0 No 0.01 
Temperature (°C)MK (Z)Trendβ (°C/10a)
Annual 4.62 Yes (+) 0.31** 
Spring 3.74 Yes (+) 0.43** 
Summer 3.96 Yes (+) 0.42** 
Autumn 2.73 Yes (+) 0.25** 
Winter 0 No 0.01 

**The significant test level at p < 0.01.

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Figure 3
Elevation-dependent warming over the Qilian Mountains at different altitudes.
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Elevation-dependent warming over the Qilian Mountains at different altitudes.

Figure 3
Elevation-dependent warming over the Qilian Mountains at different altitudes.
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Elevation-dependent warming over the Qilian Mountains at different altitudes.

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Figure 4
Significance of elevation-dependent warming for annual and seasonal mean temperature above different altitude thresholds based on the ERA5-Land reanalysis temperature from 1979 to 2017 in the QLM (°C/10a) (Note: the positive value indicates the elevation-dependent warming trend while the negative value indicates the elevation-dependent cooling trend. * and ** represent the significant levels at p < 0.05 and p < 0.01, respectively).
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Significance of elevation-dependent warming for annual and seasonal mean temperature above different altitude thresholds based on the ERA5-Land reanalysis temperature from 1979 to 2017 in the QLM (°C/10a) (Note: the positive value indicates the elevation-dependent warming trend while the negative value indicates the elevation-dependent cooling trend. * and ** represent the significant levels at p < 0.05 and p < 0.01, respectively).

Figure 4
Significance of elevation-dependent warming for annual and seasonal mean temperature above different altitude thresholds based on the ERA5-Land reanalysis temperature from 1979 to 2017 in the QLM (°C/10a) (Note: the positive value indicates the elevation-dependent warming trend while the negative value indicates the elevation-dependent cooling trend. * and ** represent the significant levels at p < 0.05 and p < 0.01, respectively).
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Significance of elevation-dependent warming for annual and seasonal mean temperature above different altitude thresholds based on the ERA5-Land reanalysis temperature from 1979 to 2017 in the QLM (°C/10a) (Note: the positive value indicates the elevation-dependent warming trend while the negative value indicates the elevation-dependent cooling trend. * and ** represent the significant levels at p < 0.05 and p < 0.01, respectively).

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Figure 5
Warming trends of annual mean temperature at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).
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Warming trends of annual mean temperature at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).

Figure 5
Warming trends of annual mean temperature at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).
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Warming trends of annual mean temperature at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).

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Figure 6
Temperature trends in spring at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).
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Temperature trends in spring at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).

Figure 6
Temperature trends in spring at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).
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Temperature trends in spring at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).

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Figure 7
Temperature trends in autumn at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).
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Temperature trends in autumn at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).

Figure 7
Temperature trends in autumn at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).
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Temperature trends in autumn at different altitude ranges from 1979 to 2019 in the QLM (°C/10a).

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Snow cover and snow depth trends

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Table 3 shows the trends of snow cover and snow depth from 1979 to 2019 over the QLM. Snow cover and snow depth showed a slight decreasing trend, which is consistent with the previous study (Jiang et al. 2016). There was a slight decrease in annual mean snow cover (−0.493%/10a, p > 0.05) and snow depth (−6.87 × 10−4m/10a, p > 0.05) from 1979 to 2019. Snow cover and snow depth in summer show a faster trend in 1984 and 1992, respectively. Annual mean snow cover and snow depth show a slight decreasing trend at all intervals with a highest decreasing trend at 2,000–2,500 m (−0.804%/10a) (Table 4). In spring and summer, the decreasing trends of snow cover and snow depth increased as the altitude increased. In autumn, the highest increasing trends of snow cover and snow depth were found at the elevation range of 4,000–4,500 m, for 0.386%/10a and 0.00044 m/10a, respectively. In winter, the highest increasing trends of snow cover and snow depth were found at the elevation range of 4,500 − 5,000 m (Table 4). Snow cover and snow depth increase with elevation in general, with the largest snow cover and snow depth found at 5,000–5,500 m (Figure 8).
Table 3

Trends of snow cover and snow depth across the QLM from 1979 to 2019

Time scalesSnow cover
Snow depth
MK (Z)Trendβ (%/10a)Mutation timeMK (Z)Trendβ (m/10a)Mutation time
Annual 0 No −0.493 No 0 No −6.87 × 10−4 No 
Spring 0 No −0.797 No 0 No −1.65 × 10−3 No 
Summer 0 No −0.210 1984 0 No −1.94 × 10−4 1992 
Autumn 0 No −0.087 No 0 No −9.6 × 10−5 No 
Winter 0 No 0.312 No 0 No 4.25 × 10−4 No 
Time scalesSnow cover
Snow depth
MK (Z)Trendβ (%/10a)Mutation timeMK (Z)Trendβ (m/10a)Mutation time
Annual 0 No −0.493 No 0 No −6.87 × 10−4 No 
Spring 0 No −0.797 No 0 No −1.65 × 10−3 No 
Summer 0 No −0.210 1984 0 No −1.94 × 10−4 1992 
Autumn 0 No −0.087 No 0 No −9.6 × 10−5 No 
Winter 0 No 0.312 No 0 No 4.25 × 10−4 No 
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Table 4

Trends of snow cover and snow depth at different altitude intervals across the QLM from 1979 to 2019

Elevation (m)Snow cover (%/10a)
Snow depth (m/10a)
AnnualSpringSummerAutumnWinterAnnualSpringSummerAutumnWinter
2,000 − 2,500 −0.044 −9.3 × 10−3 0 −2.4 × 10−3 −0.209 −7.17 × 10−5 0 0 0 −2.09 × 10−4 
2,500 − 3,000 −0.804 −0.103 −2.22 × 10−4 −0.107 −1.027 −2.7 × 10−4 −9.2 × 10−5 0 −1.33 × 10−4 −1.04 × 10−3 
3,000 − 3,500 −0.441 −0.799 −0.130 −0.135 −0.3211 −2.67 × 10−4 −8.62 × 10−4 −1.19 × 10−4 −1.47 × 10−4 −6.46 × 10−5 
3,500 − 4,000 −0.377 −0.902 −0.275 0.052 0.181 −7 × 10−4 −2 × 10−3 −2.9 × 10−4 −2.66 × 10−5 −2.27 × 10−4 
4,000 − 4,500 −0.489 −1.597 −0.494 0.386 0.258 −6.77 × 10−4 −2.55 × 10−3 −4.86 × 10−4 4.4 × 10−4 8.7 × 10−4 
4,500 − 5,000 −0.325 −1.580 −0.518 0.263 0.506 −7.1 × 10−4 −3.3 × 10−3 −5.7 × 10−4 −1.14 × 10−4 9.77 × 10−4 
5,000 − 5,500 −0.160 −4.026 −0.630 −1.018 0.114 −1.68 × 10−2 −7.2 × 10−3 −5.9 × 10−4 −1.15 × 10−3 −5 × 10−4 
Elevation (m)Snow cover (%/10a)
Snow depth (m/10a)
AnnualSpringSummerAutumnWinterAnnualSpringSummerAutumnWinter
2,000 − 2,500 −0.044 −9.3 × 10−3 0 −2.4 × 10−3 −0.209 −7.17 × 10−5 0 0 0 −2.09 × 10−4 
2,500 − 3,000 −0.804 −0.103 −2.22 × 10−4 −0.107 −1.027 −2.7 × 10−4 −9.2 × 10−5 0 −1.33 × 10−4 −1.04 × 10−3 
3,000 − 3,500 −0.441 −0.799 −0.130 −0.135 −0.3211 −2.67 × 10−4 −8.62 × 10−4 −1.19 × 10−4 −1.47 × 10−4 −6.46 × 10−5 
3,500 − 4,000 −0.377 −0.902 −0.275 0.052 0.181 −7 × 10−4 −2 × 10−3 −2.9 × 10−4 −2.66 × 10−5 −2.27 × 10−4 
4,000 − 4,500 −0.489 −1.597 −0.494 0.386 0.258 −6.77 × 10−4 −2.55 × 10−3 −4.86 × 10−4 4.4 × 10−4 8.7 × 10−4 
4,500 − 5,000 −0.325 −1.580 −0.518 0.263 0.506 −7.1 × 10−4 −3.3 × 10−3 −5.7 × 10−4 −1.14 × 10−4 9.77 × 10−4 
5,000 − 5,500 −0.160 −4.026 −0.630 −1.018 0.114 −1.68 × 10−2 −7.2 × 10−3 −5.9 × 10−4 −1.15 × 10−3 −5 × 10−4 
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Figure 8
Variability of snow cover and snow depth at different elevation ranges over the QLM.
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Variability of snow cover and snow depth at different elevation ranges over the QLM.

Figure 8
Variability of snow cover and snow depth at different elevation ranges over the QLM.
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Variability of snow cover and snow depth at different elevation ranges over the QLM.

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Relationship between snow cover/snow depth and temperature

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Table 5 shows the correlation between temperature and snow cover as well as snow depth at different altitude ranges. In general, there are negative correlations between temperature and snow cover as well as snow depth. The correlation in spring, summer, and autumn reached a significant level at p < 0.01. The smallest negative correlation coefficient was found in winter. The relationship between annual mean temperature and annual snow cover as well as snow depth showed a negative correlation with the highest significant negative correlation being −0.759 and − 0.559 at the elevation range of 5,000–5,500 m, respectively. For seasonal scales, the lowest correlation was found in winter in general. The highest negative correlation coefficient between temperature and snow cover in spring, autumn, and winter was found at the elevation range of 5,000 − 5,500 m. However, the highest negative correlation coefficient in summer was found at the elevation range of 3,500 − 4,000 m. Figure 9 shows the spatial distribution of correlation between temperature and snow cover as well as snow depth in the QLM. The green part represents the negative correlation, the red represents the positive correlation, and the small dots represent the grid points that reached the significant level at p < 0.05. In general, there is a positive correlation between the annual mean temperature and annual snow cover as well as snow depth in the northwest of the QLM, but it does not reach the significant level at p < 0.05. In addition, there is also a positive correlation between annual mean temperature and snow cover/snow depth in the Qinghai Lake and other lake areas, and it also does not reach the significant level at p < 0.05. Spring and autumn show a similar condition with annual scale variability. There was a positive correlation between summer mean temperature and summer snow cover in the northern QLM, except for some lake areas, which reached the significant level at p < 0.05. The south of the QLM was negatively correlated, but did not reach the significant level at p < 0.05. In winter, all regions show the significant level at p < 0.05 except for the Qinghai Lake and some other lakes.
Table 5

Correlation between temperature and snow cover as well as snow depth across the QLM from 1979 to 2019

Elevation (m)Snow cover
Snow depth
AnnualSpringSummerAutumnWinterAnnualSpringSummerAutumnWinter
2,000 − 2,500 −0.159 −0.396* 0 −0.455** −0.185 −0.152 −0.356* 0 −0.461** −0.189 
2,500 − 3,000 −0.351* −0.489** −0.383* −0.621** −0.363* −0.320* −0.472** −0.301 −0.613** −0.332* 
3,000 − 3,500 −0.319* −0.634** −0.644** −0.682** −0.328* −0.174 −0.545** −0.401** −0.662** −0.212 
3,500 − 4,000 −0.349* −0.670** −0.699** −0.650** −0.258 −0.298 −0.600** −0.654** −0.625** −0.201 
4,000 − 4,500 −0.481** −0.685** −0.641** −0.665** −0.423** −0.390* −0.577** −0.480** −0.584** −0.314* 
4,500 − 5,000 −0.443** −0.655** −0.584** −0.637** −0.451** −0.350* −0.511** −0.373* −0.502 −0.287 
5,000 − 5,500 −0.759** −0.803** −0.586** −0.816** −0.749** −0.559** −0.523** −0.484** −0.616** −0.480** 
Overall −0.340* −0.648** −0.653** −0.644** −0.271 −0.237 −0.551** −0.493** −0.580** −0.173 
Elevation (m)Snow cover
Snow depth
AnnualSpringSummerAutumnWinterAnnualSpringSummerAutumnWinter
2,000 − 2,500 −0.159 −0.396* 0 −0.455** −0.185 −0.152 −0.356* 0 −0.461** −0.189 
2,500 − 3,000 −0.351* −0.489** −0.383* −0.621** −0.363* −0.320* −0.472** −0.301 −0.613** −0.332* 
3,000 − 3,500 −0.319* −0.634** −0.644** −0.682** −0.328* −0.174 −0.545** −0.401** −0.662** −0.212 
3,500 − 4,000 −0.349* −0.670** −0.699** −0.650** −0.258 −0.298 −0.600** −0.654** −0.625** −0.201 
4,000 − 4,500 −0.481** −0.685** −0.641** −0.665** −0.423** −0.390* −0.577** −0.480** −0.584** −0.314* 
4,500 − 5,000 −0.443** −0.655** −0.584** −0.637** −0.451** −0.350* −0.511** −0.373* −0.502 −0.287 
5,000 − 5,500 −0.759** −0.803** −0.586** −0.816** −0.749** −0.559** −0.523** −0.484** −0.616** −0.480** 
Overall −0.340* −0.648** −0.653** −0.644** −0.271 −0.237 −0.551** −0.493** −0.580** −0.173 

*and **represent the significance level at p < 0.05 and p < 0.01, respectively.

View Large
Figure 9
Relationship between temperature and snow cover (left) and snow depth (right) across the QLM from 1979 to 2019. The black dots indicate areas that pass the significant test (p < 0.05). (a represents annual scales, b represents spring scales, c represents summer scales, d represents autumn scales, e represents winter scales). Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/wcc.2022.391.
View largeDownload slide

Relationship between temperature and snow cover (left) and snow depth (right) across the QLM from 1979 to 2019. The black dots indicate areas that pass the significant test (p < 0.05). (a represents annual scales, b represents spring scales, c represents summer scales, d represents autumn scales, e represents winter scales). Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/wcc.2022.391.

Figure 9
Relationship between temperature and snow cover (left) and snow depth (right) across the QLM from 1979 to 2019. The black dots indicate areas that pass the significant test (p < 0.05). (a represents annual scales, b represents spring scales, c represents summer scales, d represents autumn scales, e represents winter scales). Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/wcc.2022.391.
View largeDownload slide

Relationship between temperature and snow cover (left) and snow depth (right) across the QLM from 1979 to 2019. The black dots indicate areas that pass the significant test (p < 0.05). (a represents annual scales, b represents spring scales, c represents summer scales, d represents autumn scales, e represents winter scales). Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/wcc.2022.391.

Close modal

Previous studies have shown that snow cover was mainly negatively correlated with temperature and positively correlated with precipitation (Jiang et al. 2016). Therefore, the decrease (increase) of snow cover was mainly related to the decrease (increase) of precipitation or warming (cooling). This is the reason why snow cover and snow depth did not change significantly but the negative correlation between temperature and snow cover was obvious and visible.

Driving mechanism of EDW

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Previous studies have found that snow-albedo feedback, cloud-radiation effects, the downwelling effect of water vapor on longwave radiation, and aerosols can lead to EDW (Pepin et al. 2015). Snow/ice-albedo feedback is regarded as the most important mechanism for explaining EDW (Pepin et al. 2015; Johnston et al. 2018; Guo et al. 2021; Shen et al. 2021). This study mainly focuses on the influence of snow cover on the EDW. In general, there was a negative correlation between temperature and snow cover (Table 5). Warming reduces the amount of surface ice and snow in high-altitude areas, leading to a decrease in surface albedo and an increase in solar radiation absorbed by the surface, further increasing temperatures (Pepin et al. 2015). The decrease (increase) of snow cover was mainly related to the warming (cooling), which is roughly consistent with the results from a previous study (Zhang et al. 2021). Climate warming reduces the amount of surface ice and snow in high-altitude areas above 5,000 m, which leads to a decrease in surface albedo and an increase in solar radiation absorbed by the surface, further increases temperatures and reduces surface ice and snow (Pepin et al. 2015). Meanwhile, the accelerated melting of snow may affect temperature warming (Gao et al. 2021). The snow cover in winter increased, which would have increased the surface albedo, reduced solar radiation absorbed by the surface, and decreased surface temperature in winter (Shen et al. 2021). Shen et al. (2021) found that snow depth tends to decrease faster at higher elevations (p < 0.05) than at lower elevations, and warming rates are faster at higher elevations than at lower elevations. In high-altitude areas, the negative effects of temperature on snow cover have become greater. The decrease of snow cover and snow depth at higher altitudes (especially above 5,000 m) may indicate that continuous snow cover and glacier melting are intensified due to higher temperatures. This is consistent with the study that reported that temperatures have considerably increased around 5,000 m (Zhang et al. 2021).

Implications of EDW to water resources

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Snow cover and glaciers are important water resources in the Qilian Mountains (especially for the Hexi Corridor area). Snow water equivalent is the depth of snow water after the complete melting of snow. The warming of the Qilian Mountains leads to the melting of snow cover and the increase of snow water equivalent, which will also supplement the water resources in the Hexi corridor of the Qilian Mountains. Figures 10 and 11 indicate that snow depth water equivalent increases with elevation, with the largest value at 5,000–5,500 m except for summer. The seasonal snow depth water equivalent follows the order of winter > spring > autumn > summer in the QLM. Due to the continuous warming of climate, the melting of solid water in glaciers and frozen soil intensifies, increasing soil moisture and runoff in high-altitude areas (especially above 5,000 m) (Palazzi et al. 2018). EDW has significant implications for water resources over the ‘Hexi Corridor’ around the QLM. Glaciers, snow, and permafrost are concentrated in the critical elevation zone above 5,000 m, so the rate of warming above this elevation is a strong determinant of the rate of future decline of these resources (You et al. 2020). Temperature trends in the QLM are largest above 5,000 m over the QLM, so the warming above 5,000 m may lead to a more rapid increase in water resources. The increase of water resources is of great significance to the oasis surrounding the QLM.
Figure 10
Variability of snow depth water equivalent at different elevation ranges over the QLM.
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Variability of snow depth water equivalent at different elevation ranges over the QLM.

Figure 10
Variability of snow depth water equivalent at different elevation ranges over the QLM.
View largeDownload slide

Variability of snow depth water equivalent at different elevation ranges over the QLM.

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Figure 11
Variability of snow depth water equivalent at different elevations from 1979 to 2019 over the QLM.
View largeDownload slide

Variability of snow depth water equivalent at different elevations from 1979 to 2019 over the QLM.

Figure 11
Variability of snow depth water equivalent at different elevations from 1979 to 2019 over the QLM.
View largeDownload slide

Variability of snow depth water equivalent at different elevations from 1979 to 2019 over the QLM.

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Listen

The annual mean temperature showed a significant warming trend with a rate of 0.31 °C/10a. At the same time, the warming rate of annual mean temperature increased with elevation in the QLM in general. The EDW is distinctly seasonal, with the most obvious warming in summer, and the least obvious warming in winter.

The melt of snow cover has accelerated under regional warming in the QLM. Changes in snow cover were found to be an important factor contributing to the variability of EDW. Climate warming reduces the amount of snow cover in high-altitude areas, which leads to a decrease in surface albedo and an increase in solar radiation absorbed by the surface, further increasing temperatures. This new evidence can partly explain the EDW variability in the QLM, although the mechanisms remain to be determined.

EDW has a significant implication for water resources over the QLM, especially above 5,000 m. Water from snow melt is an important factor in the change of water resources in the Qilian Mountains. However, the actual impacts of EDW on regional water availability still need to be discussed in the future.

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This study was supported by the National Key Research and Development Program of China (No. 2019YFC0507403), the National Natural Science Foundation of China (No. 41621001), and the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA23060301).

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Data cannot be made publicly available; readers should contact the corresponding author for details.

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The authors declare there is no conflict.

Aguilar-Lome
J.
,
Espinoza-Villar
R.
,
Espinoza
J.-C.
,
Rojas-Acuña
J.
,
Willems
B. L.
 &
Leyva-Molina
W.-M.
2019
Elevation-dependent warming of land surface temperatures in the Andes assessed using MODIS LST time series (2000–2017)
.
International Journal of Applied Earth Observation and Geoinformation
77
,
119
–
128
.
doi:10.1016/j.jag.2018.12.013
.
Google Scholar
Crossref
Search ADS
 
Alexander
M.
 &
Gregor
B.
2020
Demystifying the use of ERA5-Land and machine learning for wind power forecasting
.
IET Renewable Power Generation
14
,
4159
–
4168
.
doi: 10.1049/iet-rpg.2020.0576
.
Google Scholar
 
Cao
B.
,
Gruber
S.
,
Zheng
D.
 &
Li
X.
2020
The ERA5-Land soil temperature bias in permafrost regions
.
The Cryosphere
14
,
2581
–
2595
.
doi: 10.5194/tc-14-2581-2020
.
Google Scholar
Crossref
Search ADS
 
Fan
M.
,
Xu
J.
,
Chen
Y.
 &
Li
W.
2021
Reconstructing high-resolution temperature for the past 40 years in the Tianshan Mountains, China based on the Earth system data products
.
Atmospheric Research
253
.
doi:10.1016/j.atmosres.2021.105493
.
Google Scholar
 
Gao
L.
,
Wei
J.
,
Wang
L.
,
Bernhardt
M.
,
Schulz
K.
 &
Chen
X.
2018a
A high-resolution air temperature data set for the Chinese Tian Shan in 1979–2016
.
Earth System Science Data
10
,
2097
–
2114
.
doi:10.5194/essd-10-2097-2018
.
Google Scholar
Crossref
Search ADS
 
Gao
Y.
,
Chen
F.
,
Lettenmaier
D. P.
,
Xu
J.
,
Xiao
L.
 &
Li
X.
2018b
Does elevation-dependent warming hold true above 5000 m elevation? Lessons from the Tibetan Plateau
.
npj Climate and Atmospheric Science
1
.
doi:10.1038/s41612-018-0030-z
.
Google Scholar
 
Gao
L.
,
Deng
H.
,
Lei
X.
,
Wei
J.
,
Chen
Y.
,
Li
Z.
,
Ma
M.
,
Chen
X.
,
Chen
Y.
,
Liu
M.
 &
Gao
J.
2021
Evidence of elevation-dependent warming from the Chinese Tian Shan
.
The Cryosphere
15
,
5765
–
5783
.
doi:10.5194/tc-15-5765-2021
.
Google Scholar
Crossref
Search ADS
 
Guo
D.
,
Sun
J.
,
Yang
K.
,
Pepin
N.
 &
Xu
Y.
2019
Revisiting recent elevation-dependent warming on the Tibetan plateau using satellite-based data sets
.
Journal of Geophysical Research: Atmospheres
124
,
8511
–
8521
.
doi:10.1029/2019jd030666
.
Google Scholar
Crossref
Search ADS
 
Guo
D.
,
Pepin
N.
,
Yang
K.
,
Sun
J.
 &
Li
D.
2021
Local changes in snow depth dominate the evolving pattern of elevation-dependent warming on the Tibetan Plateau
.
Science Bulletin
66
,
1146
–
1150
.
doi:10.1016/j.scib.2021.02.013
.
Google Scholar
Crossref
Search ADS
PubMed
 
Jiang
Y.
,
Ming
J.
,
Ma
P.
,
Wang
P.
 &
Du
Z.
2016
Variation in the snow cover on the Qilian Mountains and its causes in the early 21st century
.
Geomatics, Natural Hazards and Risk
7
,
1824
–
1834
.
doi:10.1080/19475705.2016.1176078
.
Google Scholar
Crossref
Search ADS
 
Jiang
L.
,
Tawia
H. D. F.
 &
Yi
L.
2020
Global land surface temperature change (2003–2017) and its relationship with climate drivers: AIRS, MODIS, and ERA5-Land based analysis
.
Remote Sensing
13
,
44
.
doi: 10.3390/rs13010044
.
Google Scholar
 
Jiao
L.
,
Xue
R.
,
Qi
C.
,
Chen
K.
 &
Liu
X.
2021
Comparison of the responses of radial growth to climate change for two dominant coniferous tree species in the eastern Qilian Mountains, northwestern China
.
International Journal of Biometeorology
.
doi:10.1007/s00484-021-02139-4
.
Google Scholar
 
Joaquín
M. S.
,
Emanuel
D.
,
Anna
A. P.
,
Clément
A.
,
Gabriele
A.
,
Gianpaolo
B.
,
Souhail
B.
,
Margarita
C.
,
Shaun
H.
,
Hans
H.
,
Brecht
M.
,
Diego
G. M.
,
María
P.
,
Nemesio
J. R. F.
,
Ervin
Z.
,
Carlo
B.
 &
Noël
T. J.
2021
ERA5-Land: a state-of-the-art global reanalysis dataset for land applications
.
Earth System Science Data
13
,
4349
–
4383
.
doi: 10.5194/essd-13-4349-2021
.
Google Scholar
 
Johnston
V. E.
,
Borsato
A.
,
Frisia
S.
,
Spotl
C.
,
Dublyansky
Y.
,
Tochterle
P.
,
Hellstrom
J. C.
,
Bajo
P.
,
Edwards
R. L.
 &
Cheng
H.
2018
Evidence of thermophilisation and elevation-dependent warming during the Last Interglacial in the Italian Alps
.
Scientific Reports
8
,
2680
.
doi:10.1038/s41598-018-21027-3
.
Google Scholar
Crossref
Search ADS
PubMed
 
Konstantinos
S.
,
George
V.
,
Aikaterini
V.
,
Anastasios
P.
 &
Elias
D.
2021
Delineating the relative contribution of climate related variables to chlorophyll-a and phytoplankton biomass in lakes using the ERA5-Land climate reanalysis data
.
Water Research
196
,
117053
.
Google Scholar
PubMed
 
Li
B.
,
Chen
Y.
 &
Shi
X.
2020
Does elevation dependent warming exist in high mountain Asia?
Environmental Research Letters
15
.
doi:10.1088/1748-9326/ab6d7f
.
Google Scholar
 
Lin
P.
,
He
Z.
,
Du
J.
,
Chen
L.
,
Zhu
X.
 &
Li
J.
2017
Recent changes in daily climate extremes in an arid mountain region, a case study in northwestern China's Qilian Mountains
.
Scientific Reports
7
,
2245
.
doi:10.1038/s41598-017-02345-4
.
Google Scholar
Crossref
Search ADS
PubMed
 
Luis
R. C.
 &
Johannes
S.
2020
Simulation of multi-annual time series of solar photovoltaic power: is the ERA5-Land reanalysis the next big step?
Sustainable Energy Technologies and Assessments
42
,
100829
.
Google Scholar
 
Miller
J. R.
,
Fuller
J. E.
,
Puma
M. J.
 &
Finnegan
J. M.
2021
Elevation-dependent warming in the Eastern Siberian Arctic
.
Environmental Research Letters
16
.
doi:10.1088/1748-9326/abdb5e
.
Google Scholar
 
Minder
J. R.
,
Letcher
T. W.
 &
Liu
C.
2018
The character and causes of elevation-dependent warming in high-resolution simulations of rocky mountain climate change
.
Journal of Climate
31
,
2093
–
2113
.
doi:10.1175/jcli-d-17-0321.1
.
Google Scholar
Crossref
Search ADS
 
Niu
X.
,
Tang
J.
,
Chen
D.
,
Wang
S.
 &
Ou
T.
2021a
Elevation-dependent warming over the Tibetan plateau from an ensemble of CORDEX-EA regional climate simulations
.
Journal of Geophysical Research: Atmospheres
126
(
9
),
e2020JD033997
.
doi:10.1029/2020jd033997
.
Google Scholar
 
Niu
X.
,
Tang
J.
,
Chen
D.
,
Wang
S.
,
Ou
T.
 &
Fu
C.
2021b
The performance of CORDEX-EA-II simulations in simulating seasonal temperature and elevation-dependent warming over the Tibetan Plateau
.
Climate Dynamics
57
,
1135
–
1153
.
doi:10.1007/s00382-021-05760-6
.
Google Scholar
Crossref
Search ADS
 
Palazzi
E.
,
Mortarini
L.
,
Terzago
S.
 &
von Hardenberg
J.
2018
Elevation-dependent warming in global climate model simulations at high spatial resolution
.
Climate Dynamics
52
,
2685
–
2702
.
doi:10.1007/s00382-018-4287-z
.
Google Scholar
Crossref
Search ADS
 
Pepin
N.
,
Bradley
R. S.
,
Diaz
H. F.
,
Baraer
M. E.
,
Caceres
B.
,
Forsythe
N.
,
Fowler
H.
,
Greenwood
G.
,
Hashmi
M. Z.
,
Liu
X. D.
,
Miller
J. R.
,
Ning
L.
,
Ohmura
A.
,
Palazzi
E.
,
Rangwala
I.
,
Schöner
W.
,
Severskiy
I.
,
Shahgedanova
M.
,
Wang
M. B.
,
Williamson
S. N.
 &
Yang
D. Q.
2015
Elevation-dependent warming in mountain regions of the world
.
Nature Climate Change
5
,
424
–
430
.
doi:10.1038/nclimate2563
.
Google Scholar
 
Pepin
N.
,
Deng
H.
,
Zhang
H.
,
Zhang
F.
,
Kang
S.
 &
Yao
T.
2019
An examination of temperature trends at high elevations across the Tibetan plateau: the Use of MODIS LST to understand patterns of elevation-dependent warming
.
Journal of Geophysical Research: Atmospheres
124
,
5738
–
5756
.
doi:10.1029/2018jd029798
.
Google Scholar
Crossref
Search ADS
 
Rangwala
I.
 &
Miller
J. R.
2012
Climate change in mountains: a review of elevation-dependent warming and its possible causes
.
Climatic Change
114
,
527
–
547
.
doi:10.1007/s10584-012-0419-3
.
Google Scholar
Crossref
Search ADS
 
Río
M. D.
,
Vergarechea
M.
,
Hilmers
T.
,
Alday
J. G.
,
Avdagić
A.
,
Binderh
F.
,
Bosela
M.
,
Dobor
L.
,
Forrester
D. I.
,
Halilović
V.
,
Ibrahimspahić
A.
,
Klopcic
M.
,
Lévesque
M.
,
Nagel
T. A.
,
Sitkova
Z.
,
Schütze
G.
,
Stajić
B.
,
Stojanović
D.
,
Uhl
E.
,
Zlatanov
T.
,
Tognetti
R.
 &
Pretzsch
H.
2021
Effects of elevation-dependent climate warming on intra- and inter-specific growth synchrony in mixed mountain forests
.
Forest Ecology and Management
479
.
doi:10.1016/j.foreco.2020.118587
.
Google Scholar
 
Rottler
E.
,
Kormann
C.
,
Francke
T.
 &
Bronstert
A.
2019
Elevation-dependent warming in the Swiss Alps 1981–2017: features, forcings and feedbacks
.
International Journal of Climatology
39
,
2556
–
2568
.
doi:10.1002/joc.5970
.
Google Scholar
Crossref
Search ADS
 
Shen
L.
,
Zhang
Y.
,
Ullah
S.
,
Pepin
N.
 &
Ma
Q.
2021
Changes in snow depth under elevation-dependent warming over the Tibetan Plateau
.
Atmospheric Science Letters
22
.
doi:10.1002/asl.1041
.
Google Scholar
 
Tayfur
G.
 &
Yacoub
E.
2019
Trend analysis of temperature and precipitation in Trarza region of Mauritania
.
Journal of Water and Climate Change
10
,
484
–
493
.
doi:10.2166/wcc.2018.007
.
Google Scholar
 
Thakuri
S.
,
Dahal
S.
,
Shrestha
D.
,
Guyennon
N.
,
Romano
E.
,
Colombo
N.
 &
Salerno
F.
2019
Elevation-dependent warming of maximum air temperature in Nepal during 1976–2015
.
Atmospheric Research
228
,
261
–
269
.
doi:10.1016/j.atmosres.2019.06.006
.
Google Scholar
Crossref
Search ADS
 
Toledo
O.
,
Palazzi
E.
,
Cely Toro
I. M.
 &
Mortarini
L.
2021
Comparison of elevation-dependent warming and its drivers in the tropical and subtropical Andes
.
Climate Dynamics
.
doi:10.1007/s00382-021-06081-4
.
Google Scholar
 
Tudoroiu
M.
,
Eccel
E.
,
Gioli
B.
,
Gianelle
D.
,
Schume
H.
,
Genesio
L.
 &
Miglietta
F.
2016
Negative elevation-dependent warming trend in the Eastern Alps
.
Environmental Research Letters
11
.
doi:10.1088/1748-9326/11/4/044021
.
Google Scholar
 
Wang
L.
,
Chen
R.
,
Han
C.
,
Wang
X.
,
Liu
G.
,
Song
Y.
,
Yang
Y.
,
Liu
J.
,
Liu
Z.
,
Liu
X.
,
Guo
S.
 &
Zheng
Q.
2019
Change characteristics of precipitation and temperature in the Qilian Mountains and Hexi Oasis, Northwestern China
.
Environmental Earth Sciences
78
.
doi:10.1007/s12665-019-8289-x
.
Google Scholar
 
Wen
K.
,
Ren
G.
,
Li
J.
,
Zhang
A.
,
Ren
Y.
,
Sun
X.
 &
Zhou
Y.
2019
Recent surface air temperature change over mainland China based on an urbanization-bias adjusted dataset
.
Journal of Climate
32
,
2691
–
2705
.
doi:10.1175/jcli-d-18-0395.1
.
Google Scholar
Crossref
Search ADS
 
Williamson
S. N.
,
Zdanowicz
C.
,
Anslow
F. S.
,
Clarke
G. K. C.
,
Copland
L.
,
Danby
R. K.
,
Flowers
G. E.
,
Holdsworth
G.
,
Jarosch
A. H.
 &
Hik
D. S.
2020
Evidence for elevation-dependent warming in the St. Elias Mountains, Yukon, Canada
.
Journal of Climate
33
,
3253
–
3269
.
doi:10.1175/jcli-d-19-0405.1
.
Google Scholar
Crossref
Search ADS
 
Wu
Z.
,
Feng
H.
,
He
H.
,
Zhou
J.
 &
Zhang
Y.
2021
Evaluation of soil moisture climatology and anomaly components derived from ERA5-Land and GLDAS-2.1 in China
.
Water Resources Management
35
,
629
–
643
.
doi: 10.1007/s11269-020-02743-w
.
Google Scholar
Crossref
Search ADS
 
Xu
J. T.
,
Ma
Z. Q.
,
Yan
S. K.
 &
Peng
J.
2022
Do ERA5 and ERA5-Land precipitation estimates outperform satellite-based precipitation products? A comprehensive comparison between state-of-the-art model-based and satellite-based precipitation products over mainland China
.
Journal of Hydrology
605
,
127353
.
Google Scholar
Crossref
Search ADS
 
Yi
S.
,
He
Y.
,
Guo
X.
,
Chen
J.
,
Wu
Q.
,
Qin
Y.
 &
Ding
Y.
2018
The physical properties of coarse-fragment soils and their effects on permafrost dynamics: a case study on the central Qinghai–Tibetan Plateau
.
The Cryosphere
12
,
3067
–
3083
.
doi:10.5194/tc-12-3067-2018
.
Google Scholar
Crossref
Search ADS
 
You
Q.
,
Chen
D.
,
Wu
F.
,
Pepin
N.
,
Cai
Z.
,
Ahrens
B.
,
Jiang
Z.
,
Wu
Z.
,
Kang
S.
 &
AghaKouchak
A.
2020
Elevation dependent warming over the Tibetan Plateau: patterns, mechanisms and perspectives
.
Earth-Science Reviews
210
.
doi:10.1016/j.earscirev.2020.103349
.
Google Scholar
 
Zhang
M.
,
Wang
B.
,
Cleverly
J.
,
Liu
D. L.
,
Feng
P.
,
Zhang
H.
,
Huete
A.
,
Yang
X.
 &
Yu
Q.
2020
Creating new near-surface air temperature datasets to understand elevation-dependent warming in the Tibetan Plateau
.
Remote Sensing
12
.
doi:10.3390/rs12111722
.
Google Scholar
 
Zhang
R.
,
Xu
Z.
,
Zuo
D.
 &
Ban
C.
2021
Variation of snow cover in the Nyang River basin of southeastern Tibetan Plateau, China
.
Journal of Water and Climate Change
12
,
3505
–
3517
.
doi:10.2166/wcc.2021.037
.
Google Scholar
Crossref
Search ADS
 
Zhang
H. B.
,
Immerzeel
W. W.
,
Zhang
F.
,
de Kok
R. J.
,
Chen
D. L.
 &
Yan
W.
2022
Snow cover persistence reverses the altitudinal patterns of warming above and below 5,000 m on the Tibetan Plateau
.
Science of the Total Environment
803
,
149889
.
doi: 10.1016/j.scitotenv.2021.149889
.
Google Scholar
Crossref
Search ADS
PubMed
 
Zhao
P.
 &
He
Z.
2022
A first evaluation of ERA5-Land reanalysis temperature product over the Chinese Qilian Mountains
.
Frontiers in Earth Science
10
,
907730
.
doi: 10.3389/feart.2022.907730
.
Google Scholar
Crossref
Search ADS
 
Zuo
Z.
,
Xiao
D.
 &
He
Q.
2021
Role of the warming trend in global land surface air temperature variations
.
Science China Earth Sciences
64
,
866
–
871
.
doi:10.1007/s11430-020-9775-8
.
Google Scholar
Crossref
Search ADS
 
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