Research papersQuantifying glacier mass change and its contribution to lake growths in central Kunlun during 2000–2015 from multi-source remote sensing data
Introduction
The Qinghai–Tibet Plateau (QTP), which is also known as the “third pole” of the Earth, contains a large number of alpine glaciers and lakes, both of which are sensitive indicators of climate change. Studies have shown that glaciers across the QTP and its surroundings have experienced apparent mass loss (Yao et al., 2012, Zhou et al., 2018) since the 1970s, while most of the lakes in the interior of the QTP have been expanding (Song et al., 2013, Lei et al., 2013, Liu et al., 2014). After the 2000s, both the glaciers and lakes have shown an accelerated change tendency (Zhang et al., 2011, Song et al., 2013, Lei et al., 2014, Jiang et al., 2017, Kääb et al., 2015, Brun et al., 2017). Within the QTP, the variation of the glaciers and lakes not only plays an important role in regulating the regional water balance, especially for a closed lake basin, but also brings several potential risks for the ecological environment of the plateau (Tong et al., 2016). Therefore, conducting a joint investigation into the glacier mass change and lake water budget of the QTP is of great significance.
For monitoring glacier mass change, field measurement (also known as the glaciological method) is not only laborious and time-consuming, but is also a very challenging task, due to the harsh natural environment. With the rapid development of remote sensing technology, the geodetic method based on the differencing of various topography data acquired in different epochs has been widely used (Gardelle et al., 2013, Kääb et al., 2015, Brun et al., 2017, Li et al., 2017b). Compared to a considerable number of glacier investigations in the QTP’s surroundings (e.g., the Himalaya/Karakoram mountains), the inner QTP glaciers have received relatively little attention, especially in the northern region. Over the whole inner QTP, geodetic-based estimates suggest that the glaciers, overall, have been in a negative state of mass budget for 2000–2016, with an apparent acceleration after 2008. For example, from a comparison of ASTER digital elevation models (DEMs), Brun et al. (2017) reported an overall mass change of −0.14 ± 0.07 m w.e./a for 2000–2016, for which the rate of mass loss increased from −0.01 ± 0.19 m w.e./a for 2000–2008 to −0.24 ± 0.20 m w.e./a for 2008–2016, despite a substantial overlap of error bars. Moreover, based on ICESat laser altimetry data of 2003–2008/09, Gardner et al., 2013, Brun et al., 2017 also reported a slightly negative mass balance of −0.01 ± 0.10 m w.e./a and −0.06 ± 0.06 m w.e./a, respectively. At a sub-region spatial scale, the existing studies have revealed a strongly heterogeneous pattern of glacier mass change within the QTP. For example, the glaciers in the northwest of the QTP experienced relatively weak surface thinning (e.g., −0.07 ± 0.14 m/a over the Ulugh Muztagh region (Fig. 1) for 1975–2000) (Zhou et al., 2018) or even pronounced thickening (e.g., 0.44 ± 0.26 and 0.86 ± 0.31 m/a Zengse Kungri and Songzhi Peak for 2003–2009) (Neckel et al., 2014, Phan et al., 2017). In contrast, the central QTP (e.g., Tanggula Mountains) suffered from serious surface down-wasting, with a rate of elevation change increasing from −0.26 ± 0.14 m/a for 1976–2000 (Zhou et al., 2018) to −0.68 ± 0.29 or −0.88 ± 0.41 m/a for 2003–2009 (Neckel et al., 2014, Phan et al., 2017). In particular, in the north of the QTP (i.e., the central Kunlun-KekeXili region), the existing results of the glacier elevation change exhibited a contradictory situation, e.g., −0.90 ± 0.28 m/a in Neckel et al. (2014) versus 0.03 ± 0.47 m/a in Phan et al. (2017) for 2003–2009. However, despite being a 1° × 1° geographic grid, Brun et al.’s (2017) results implied that this region may have been in a state of mass deficit for 2000–2016. Accordingly, how the glaciers of this region respond to climate variation at a fine spatial scale is still an issue that deserves to be solved, especially considering the spatial distribution of the glaciers being relatively dispersed.
In addition, by exploying ICESat (Ice, Cloud, and land Elevation Satellite) data, previous studies have shown that the vast majority of lakes in the northern part of the QTP have experienced varying degrees of water-level rise, with the rate ranging from 0.04 m/a to 0.53 m/a for 2003–2009 (Zhang et al., 2011, Phan et al., 2012, Song et al., 2014b). Recent studies analyzing ICESat data and CryoSat-2 radar altimetry data revealed that, for the period of 2003–2014/2015, the lakes of the northern QTP, as a whole, experienced the most significant water-level rise compared to other regions within the plateau (Song et al., 2015a, Song et al., 2015b, Jiang et al., 2017). For example, for KekeXili Lake and LexieWudan Lake, the two largest glacier-fed closed lakes, the rates of water-level rise increased from 0.29 m/a and 0.34 m/a for 2003–2009 (Song et al., 2014b) to 0.40 m/a and 0.45 m/a for 2010–2015 (Jiang et al., 2017), respectively. However, for the period corresponding to the glacier monitoring of this study (i.e., 2000–2015), the lake volume change, which is the most direct reflection of regional surface water storage, is still unknown, although Zhang et al. (2017) estimated the lake volume change across the whole QTP from the early 1970s to 2015. To estimate lake volume change, specific lake water levels at the beginning and end of the observation period must be known. In particular, for a study period in which lake water levels are unavailable (e.g., 2000–2015), in general, the previous studies first established a functional relationship between lake area and water level for a certain period with continuous water-level measurements (e.g., 2003–2009) by using a first-order (linear) or second-order regression model, and then calculated the unmeasured lake water levels based on the corresponding lake areas (Song et al., 2013, Song et al., 2014a, Zhang et al., 2013a). Nevertheless, in this study, we directly build on the statistical relationship between lake level change and area change, to estimate the unmeasured water level and further calculate the lake volume change.
Once the lake storage change is known, we can quantitatively evaluate the contribution of glacier meltwater to lake expansion in a closed lake basin, based on an assumption that the glacier mass loss is completely transferred to the lakes (Lei et al., 2012, Lei et al., 2013). With regard to the causes of lake expansion, especially the contribution of glacier meltwater, Song et al., 2014b, Zhang et al., 2017, from the angle of qualitative analysis and quantitative evaluation, respectively, concluded that for the whole of the QTP glacier meltwater plays an important but not a dominant role in lake growth. In addition, for local areas, quantitative evaluations, based on either integrated physical models (e.g., hydrological models, glacier-melt models, and heat-balance equations) or an indirect estimate of glacier meltwater (based on the hypothesis mentioned above) have mainly concentrated on the central QTP, due to the abundant in-situ observation data, e.g., in Seling Co Lake (Lei et al., 2013, Tong et al., 2016) and Nam Co Lake (Wu et al., 2014a, Wu et al., 2014b, Li et al., 2017a). However, to date, the contribution of glacier meltwater to lake water-level rise is still unclear in the north of the QTP.
In view of the above issues, the aims of this study included two main aspects. Firstly, by using SPOT-6/7 stereo imagery acquired in 2015/16 and Shuttle Radar Topography Mission (SRTM) DEMs from 2000, we generated the first high-resolution map of glacier elevation change in the central Kunlun-KekeXili region (including seven sampling regions) and obtained a region-wide glacier mass change figure based on the geodetic method. Secondly, we estimated the water storage change of two typical glacier-fed closed lakes (i.e., LexieWudan Lake and KekeXili Lake) between 2000 and 2015 using the strategy mentioned above. We then roughly quantified the contribution of glacier meltwater to the lake expansion based on the assumption mentioned above. The results of this work will help to improve the understanding of the responses of glaciers and lakes to climate change and the relationship between them in the northern part of the QTP.
Section snippets
Study region
The central Kunlun-KekeXili region is located in the north of the QTP, with an average altitude of above 5000 m. Owing to the harsh natural environment characterized by a cold and dry climate, this area is sometimes referred to as “no man’s land”. On this plateau, the mean annual air temperature is, on average, about −10 °C, and the annual precipitation is 173–494 mm (Li et al., 1996). More than 90% of the precipitation occurs from May to September, implying that the glaciers in this region
SPOT-6/7 imagery
As an extension of the SPOT mission, two advanced satellites—SPOT-6 and SPOT-7 (pushbroom imaging systems) were successfully launched on September 9, 2012, and June 7, 2014. Both satellites were designed with the same orbit setting (sun-synchronous orbit with a 98.2° inclination at an altitude of 694 km) and phased 180° from each other, allowing the satellites to revisit anywhere with a minimum period of one day. The panchromatic and multispectral images are acquired simultaneously with a
Glacier boundaries
In this study, we did not generate our own glacier outlines, but directly used the Second Chinese Glacier Inventory (CGI-2, version 1.0) (Liu et al., 2014), which was produced based on Landsat TM/ETM+ images taken mainly during 2006–2010 (Guo et al., 2015). The position accuracy of the CGI-2 boundaries is about ±10 m for manually revised clean-ice outlines (Guo et al., 2015). Over this study area, the CGI-2 outlines represent the glacier extent in 2005. In consideration of possible glacier
Glacier thickness change and mass balance
From Fig. 3, it can be seen that almost all of the glaciers among the investigated areas experienced different degrees of surface thinning near their toes, except for the surging glaciers. Based on the median elevation of each glacier from the Randolph Glacier Inventory (RGI) Version 6.0 (RGI Consortium, 2017), we approximately estimated the position of the region-wide glacier equilibrium line by using the mean of them and further divided glaciers areas into two parts—the ablation zones and the
The impact of penetration depth on glacier mass change
Since the penetration depth of the X-band radar was not considered, the penetration depth of the C-band radar we obtained is likely underestimated. Prior studies have shown that, for dry snow, the penetration depth of X-band radar can reach 2–6 m in Antarctica (Groh et al., 2014, Seehaus et al., 2015, Zhao and Floricioiu, 2017) and 4–6 m in the Alps (Berthier et al., 2016, Dehecq et al., 2016). Especially in the QTP and its surroundings, the X-band average penetration depth in the accumulation
Conclusion
In this study, based on the geodetic method, we calculated the region-wide glacier mass balance for 2000–2015/16 in the central Kunlun-KekeXili region (including seven sample regions) using SPOT-6/7 stereo imagery and the SRTM DEM. The results reflect a heterogeneous pattern at the sub-region spatial scale. The most significant glacier mass loss, at rates of −0.22 ± 0.10 and −0.21 ± 0.10 m w.e./a, occurred in the Malan and Xinqingfeng mountains, respectively. Moreover, except for the
Declaration of Interest statement
None.
Acknowledgments
We are grateful to Dr. Jiang Liguang of the Technical University of Denmark for providing the CryoSat-2 results and giving some constructive suggestions as to how we could improve this manuscript. The Second Chinese Glacier Inventory was provided by the Environmental and Ecological Science Data Center for West China (http://westdc.westgis.ac.cn). The SRTM C-band DEM and the Landsat 5/7/8 images were freely downloaded from the USGS. We would also like to thank the DLR for providing the SRTM
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