The HMA region refers to a broad high-altitude region in South and Central Asia that covers the whole Tibetan Plateau and adjacent mountain ranges, including the eastern Hindu Kush, western Himalaya, eastern Himalaya, central Himalaya, Karakoram, western Pamir, Pamir-Alay, northern and western Tien Shan, Dzhungarsky Alatau, western Kunlun Shan, Nyainqêntanglha, Gangdise Mountains, Hengduan Shan, Tibetan interior mountains, Tanggula Shan, eastern Tibetan mountains, Qilian Shan, eastern Kunlun Shan, Altun Shan, eastern Tien Shan, central Tien Shan, and eastern Pamir (Figs. 1 and 6a). It extends from 26 to 45∘ N and from 67 to 105∘ E, and the altitude of the plateau is about 4500 m on average (Baumann et al., 2009). It is made up of alternating mountains, valleys, and rivers, and the terrain is fragmented, showing a decreasing terrain from northwest to southeast. The HMA region has a series of east–west mountain ranges that occupy most of the area. Among these, Tanggula Shan lies in the central part of HMA, with an altitude of over 6000 m. The heights of the 15 highest mountains in the Himalayas are greater than 8000 m, while the peaks of the mountains in the northern plateau are greater than 6500 m. The north–south mountain ranges are mainly distributed in the southeast of the plateau and near the Hengduan Mountains area. These two groups of mountains constitute the geomorphic framework and control the basic pattern of the plateau landform. Continuous and discontinuous permafrost has developed on the higher land and north-facing slopes.

The HMA region is the source of several of Asia's major rivers, including the Yellow, Yangtze, Indus, Ganges, Brahmaputra, Irrawaddy, Salween, and Mekong. They play a crucial role in downstream hydrology and water availability in Asia (Immerzeel and Bierkens, 2010). Most glaciers in the Tibetan Plateau are retreating, except for the western Kunlun (Neckel et al., 2014; Kääb et al., 2015) and the Karakoram, where a slight mass gain is occurring (Bolch et al., 2012; Gardner et al., 2013). Moreover, glaciers in different mountain ranges show contrasting patterns. Local factors (e.g., exposure, topography, and debris coverage) may partly account for these differences, but the spatial and temporal heterogeneity of both the climate and degree of climate change may be the main reason. Glacial lakes are formed and develop temporally with the retreat or thinning of glaciers and are directly or indirectly fed by glacier meltwater. They are located within 10 km of the nearest glacier terminus (Wang et al., 2013; Zhang et al., 2015).

The HMA climate is under the combined and competing influences of the East Asian and South Asian monsoons and of the westerlies (Schiemann et al., 2009). This unique geographical position produces an azonal plateau climate characterized by strong solar radiation, low air temperatures, large daily temperature variations, and small differences between annual mean temperatures (Yao et al., 2012). The annual mean temperature is 1.6 ∘C, with the lowest temperature of -1 to -7 ∘C occurring in January and the highest temperature of 7 to 15 ∘C occurring in July. The cumulative annual precipitation is about 413.6 mm.

A total of 40 481 satellite images, including Landsat 5 TM imagery during 2008–2011, Landsat 7 ETM+ imagery in 2012, and Landsat 8 Operational Land Imager (OLI) during the period 2013–2017, were available in GEE and were used to produce the annual glacial-lake maps over the entire HMA (Fig. 2). Here, when Landsat 5 or 8 data were available, Landsat 7 ETM+ imagery with Scan Line Corrector (SLC)-off gaps was generally excluded due to the artifacts induced by the slatted appearance of the original images, but these were exclusively used for the glacial-lake mapping in 2012 since no other Landsat data were acquired that year. For the years before 2008, all the available Landsat 5 TM data in each year (e.g., 2004, 2005, 2006, and 2007) do not fully cover the HMA region.

The SLC-off condition of Landsat ETM+ introduces artifacts because the slatted appearance of the original images is occasionally carried into the glacial-lake map in 2012. Techniques to fill the SLC-off gaps exist, but these create artificial values that will result in false detections of water (Chen et al., 2011). Considering the strong spatial and temporal variability in glacial lakes such as supraglacial lakes, techniques that merge data from one or more SLC-off fill scenes for generation of a gap-free image require careful use, even when using the thousands of Landsat ETM+ images. It is noted that water mapping using time series images at large scales usually avoids the use of such techniques (Mueller et al., 2016). Therefore, Landsat 7 ETM+ data with an intensive slatted appearance are not suitable for the classification of numerous glacial lakes. In this study, because the only useable data source for 2012 was Landsat 7 ETM+, to ensure continuity of annual data from 2008 to 2017, we applied our best efforts to manual extraction of the glacial lakes from the 2012 ETM+ images with the highest possible accuracy.

One effective solution to reduce the influence of seasonal lake fluctuations on the mapping is to map glacial lakes and measure their long-term changes during stable seasons, when the lake extents are minimally affected by meteorological conditions and glacier runoff. Here, based on analyses of the mapping times of glacial lakes in different regions, the selected time series of Landsat data were generally from July to November. During this period of each year, the Landsat imagery featured lower perennial snow coverage. Following glacier runoff and precipitation, the area of a glacial lake is large, and changes in this area will be small (Nie et al., 2017; Chen et al., 2017; Zhang et al., 2015). These lakes may also reach their maximum extent around the end of the glacier ablation season (June to August) (Gardelle et al., 2013; Liu et al., 2014), except in the central and eastern Himalaya, where peak ablation extends into post-monsoon September and October. In monsoon-affected areas such as Nepal and Bhutan, monsoon cloud cover from July to mid-September means that clear-sky images can mostly only be obtained from late September to November. The southeastern Tibet region is problematic not only because the observation season is short but also as a result of abundant cloud cover, which is formed by the warm and humid airflow raised by the topography (Zhang et al., 2020; Umesh et al., 2018; Qiao et al., 2016).

As the most highly variable glacial lakes in the study area, supraglacial lakes change preferentially in the year, showing an increase in area during the pre-monsoon and rising to their peak area in the early monsoon (June to July) (Miles et al., 2017a, b). Although the selected image seasons are slightly different due to the meteorological conditions in different regions, they all comply with the same criterion that the lakes were in clear-sky images having little snow coverage. This ensured the initial reliability of the mapping of glacial lakes through the GEE cloud-computing platform. If no valid observations could be obtained, then the optimal mapping time needed to be broadened during the whole year.

To further increase data availability and also as the basis for data selection in the periods beyond the optimum mapping time, we set two criteria for the selection of imagery with valid observations over the potential glacial-lake area by using the cloud-score functions in GEE, including (i) cloud cover being less than 20 % in the 10 km buffer around each glacier outline of a Landsat scene or (ii) less than 20 % cloud cover for the entire scene. The cloud-score functions in GEE may have significant difficulty in detecting clouds in mountain headwaters with high snow and ice cover, where large amounts of snow and ice are likely to be identified as clouds. However, in this study, it was considered better to use much stricter criteria to filter out a larger number of images with lots of clouds or objects that look like clouds (snow or ice) to finally select only images with good observations.

For the development of the Hi-MAG database, we applied a systematic glacial-lake detection method that comprised two steps: initial glacial-lake extraction and subsequent manual refinement of these lake-mapping results. The main procedures for glacial-lake mapping using Landsat data, as shown in Fig. 3, are as follows. (i) The Landsat top-of-atmosphere data were clipped according to the extent of the glacier buffers and assembled into a time series dataset. (ii) Poor-quality observations were identified. These included areas affected by clouds, cloud shadow, topographic shadow, and SLC-off gaps. Here, we used the Fmask routine (Zhu and Woodcock, 2012) to detect the clouds and cloud shadows in the imagery. Fmask has the advantage of being able to process a large number of images in a computationally efficient way. Topographic shadows are located in the areas where the sunlight is blocked. Generally, on the dark side of high mountains, the surface gradients are great, and the terrain reliefs are small. Therefore, topographic shadows were masked using the slopes (larger than 10∘) and shaded relief values (less than 0.25) calculated from Shuttle Radar Topography Mission (SRTM) data (Li and Sheng, 2012; Quincey et al., 2007). This removes a considerable number of mountain shadows that have a similar spectral reflectance as water bodies. However, the SRTM digital elevation model (DEM) was generated in 2000, which is different from the acquisition time of the Landsat images used for the glacial-lake mapping in this study. The derived slopes and shaded relief cannot therefore fully represent the conditions on the date a given Landsat scene was acquired. As a consequence, some lakes that have grown at steep glacier tongues may be masked, and some mountain shadows that interfere with the mapping results of glacial lakes from GEE still remain, leading to the fact that glacial lakes in steep areas are omitted, and residual shadows are misclassified as glacial lakes. As for the SLC-off gaps in the ETM+ images, lakes outside the gaps will be accurately classified, but if they are covered by gaps, then they will be misclassified. Errors caused by striped gaps in Landsat ETM+ data were manually corrected using additional high-quality scenes across the whole year with the assistance of images from adjacent years. (iii) The modified normalized difference water index (MNDWI) was calculated (Xu, 2006). (iv) The potential glacial-lake areas were extracted by applying an adaptive MNDWI threshold (Li and Sheng, 2012). The minimum number of water pixels used to define a glacial lake in an image is inconsistent in different studies. For example, Zhang et al. (2015) set the smallest detectable glacial lakes in the Third Pole as being larger than 0.0027 km2 (3 connected pixels) using the Landsat TM and ETM+ data. Nie et al. (2017) selected 0.0081 km2 (9 connected pixels) as the minimum mapping unit to map glacial lakes in the Himalayas. Other studies have set the minimum threshold areas as 0.001 km2 (Salerno et al., 2012), 0.002 km2 (Wang et al., 2013), 0.0036 km2 (Luo et al., 2020), 0.0054 km2 (X. Wang et al., 2020), or 0.01 km2 (Li et al., 2020). A smaller minimum mapping unit will detect more glacial lakes. However, the uncertainty this brings will also be larger than using a larger threshold at the same resolution (Salerno et al., 2012). Our results demonstrate that a lake area covering fewer than 9 water pixels will have an area error of greater than 50 % (see Sect. 4). Given the uncertainty in the areas of glacial lakes and the spatial resolution of Landsat data, in this study, glacial lakes larger than 9 pixels (≥0.0081 km2) were considered to be the minimum mapping unit. (v) Manual inspection and refinement of individual glacial lakes were conducted, and the related attributions were added for each lake.

Based on the automated processing, nearly 60 % of glacial lakes in each year can be correctly classified. Of the other lakes that were not properly classified, 30 % were missed, and 10 % were misclassified. For such a large-scale area that is characterized by various and complex climatic, geological, and terrain conditions, this classification method is simple but effective. The results are also reasonable since they provide very low commission errors. To ensure the quality of the inventory, strict quality control was conducted to visually inspect and correct the mapping errors after the automated processing using GEE. False lake features, mainly identified as mountain shadows and river segments, were manually removed by overlapping mapped lake shorelines in the source Landsat imagery and higher-resolution imagery in Google Earth. Some glacial lakes may be covered by ice and clouds for years, grow at steep glacier tongues, or show heterogeneous reflectance with the surrounding backgrounds. For these missing glacial lakes, their boundaries were edited further using ArcGIS. Furthermore, a cross-check and modification were conducted for each glacial lake based on the lake-mapping results in conjunction with multi-temporal Landsat imagery. Here, all the Landsat imagery that was used for the inspection was downloaded manually from the United States Geological Survey (USGS) Earth Explorer website (https://earthexplorer.usgs.gov/, last access: 30 September 2020). The outputs for each lake polygon include information about the lake type, elevation, Euclidian distance to the nearest glacier terminus, area, and perimeter. Note that if there was more than one suitable satellite image in a year, the image with the lowest cloud cover was selected for the calculation of the area and perimeter of a given lake. Each mountain range was characterized individually by utilizing the mountain boundary shapefile in HMA (http://geo.uzh.ch/~tbolch/data/regions_hma_v03.zip, last access: 4 December 2020).

Based on the final generated lake inventory data, we used the slope of a linear regression of the lake area (over the grid cells of 1∘ × 1∘) versus mapping year to qualify the yearly changes in lake area during the study period. The change analysis used the Theil–Sen estimator, which chooses the median slope of all the derived fitted lines and can effectively represent long-term area changes due to its robustness for trend detection and its insensitivity to outliers. It is also useful for the elimination of effects arising from differences in sensor performance for the mapping of glacial lakes (Sen, 1968; Song et al., 2018).

Although all the lakes were manually checked and edited, due to the limitation of available images and other factors, the conditions for glacial-lake mapping were not perfectly consistent for each year. For example, the image dates were not consistent across the whole HMA region because of atmospheric disturbances, and there were also influences from varying lake characteristics, image quality (Bhardwaj et al., 2015; Thompson et al., 2012), ice, and shadows that obscured the lakes, which all contributed to detection errors in the lake extent and their annual variation. Generally, these errors were objective and acceptable as a result of the nature of the limited remote-sensing data. For this study, because we used time series data covering a period of 10 years for the estimation of annual changes in lake area and also because the errors only account for a small proportion of the total glacial-lake area for each year, the errors in the observed lake area caused by these different effects do not appear to affect the trends in the statistical results. In addition to the Theil–Sen estimator, a Mann–Kendall trend test was used to detect and further confirm the statistical confidence of the linear regression results, and all the estimated trends were found to fall within the 90 % confidence intervals. The upper and lower change estimates satisfying the 90 % confidence interval for the slope were also derived over the whole HMA region (Fig. A2).

The area coverage of glacial lakes increased by 90.14 km2 in the period 2008–2017, a 6.90 % increase relative to 2008 (1305.59±213.99 km2) (Fig. 5a). A Theil–Sen regression fit to all the data showed a mean expansion rate of 12 km2 a-1 for the 10-year record, as shown in Fig. 5a. Meanwhile, the estimated changes in glacial-lake number from 2008 (12 593 lakes) to 2017 (15 348 lakes) showed an average increase of 306 lakes a-1. The steeper percentage increase in the number of lakes (22.33 %) compared to the slower expansion of their area (8.79 %) based on their linear-fit trends shows that many small glacial lakes formed over this period. The number of lakes increased most rapidly in areas beyond 4400 m above sea level (a.s.l.), especially beyond 5300 m (Fig. 5b). The increase in proglacial lakes was concentrated above 4900 m (Fig. 5c). Unconnected glacial lakes grew very slightly in total area below 4400 m (Fig. 5d) but increased notably more at higher elevations. Glaciers are retreating and thinning at ever-higher elevations (Nie et al., 2017), causing the formation of new supraglacial lakes at high elevations, the expansion of existing ice-contact lakes, and the detachment of glaciers from some lakes.

Annual changes in glacial lakes were further analyzed spatially using a 1∘ × 1∘ grid over 22 mountain regions (Fig. 6a) using Theil–Sen regression analysis. An analysis of the mountain-wide lake area loss or gain from 2008 to 2017 was conducted (Table A1). Negative or undiscernible changes in glacial-lake area were observed in the eastern Tien Shan, eastern Hindu Kush, Hengduan Shan, and eastern Tibetan mountains (Fig. 6b), thus reducing the otherwise overall increasing glacial-lake area in HMA. The eastern Hindu Kush lost 2.8 km2 of lake area (Table A1), with a negative area change (-0.43 km2 a-1) near 35∘ N, 73∘ E. Glacial lakes in the Nyainqêntanglha and the Gangdise Mountains exhibited area loss and gain in different regions. In contrast, the central and eastern Himalaya and central Tien Shan showed rapid increases in lake area. Between 2008 and 2017, the central Himalaya's glacial-lake area increased by 27.09 km2 (Table A1), exhibiting both a high density of 47 glacial lakes per 100 km2 in 2017 (Fig. A3) and rapid growth, +0.94 km2 a-1, in lake area due to retreat and thinning of debris-covered glaciers (Song et al., 2016). Moderate area gains occurred along most of the western Kunlun and Tanggula Shan, e.g., +0.38 km2 a-1 in Tanggula Shan. The areas of glacial lakes in Pamir-Alay, eastern Pamir, and eastern Kunlun Shan were spatially and temporally invariant across the whole observation record.

We found that glacial lakes exhibited different expansion trends for different lake types, and supraglacial and ice-marginal lakes have relatively few coverage areas comparing with proglacial and unconnected lakes (Fig. 6b, c). In the Nyainqêntanglha and central Himalaya, around half of the glacial-lake area consisted of proglacial lakes, where most growth occurred. In the negative-lake-growth (shrinkage) regions of the eastern Tibetan mountains and Hengduan Shan, unconnected glacial lakes were dominantly occupied. As the interaction with a glacier gradually weakens, part of the water source supplied by that glacier is reduced, and when combined with the effects from atmospheric warming and a decrease in precipitation, this means that regions mainly consisting of unconnected glacial lakes show a trend of decreasing area. Proglacial lakes contributed approximately 62.87 % (56.67 km2) to the total area increase over HMA (Tables A1 and A2). Proglacial lakes in the central Himalaya, eastern Himalaya, and western Himalaya accounted for 36.27 % (32.70 km2) of the total area increase. In general, proglacial lakes are the main contributor to recent lake evolution in HMA.

We also noted that the large area growth of lakes occurred in areas with a relatively large proportion of small glacial lakes, and this was mainly due to the rapid growth of existing lakes and the formation of new lakes (Fig. 6d). For example, in some areas of the central and eastern Himalaya and Nyainqêntanglha that have large annual increases in lake area (greater than 0.23 km2 a-1), glacial lakes with a size of less than 0.16 km2 occupied more than 30 % of the total area (Table A3). In particular, in Nyainqêntanglha, the area of small glacial lakes (≤0.16 km2) accounted for 69.47 % of the total area.

To explore factors that have potentially influenced the glacial-lake distribution across HMA, we focus on proglacial and supraglacial lakes, for which the changes are closely related to glaciers, and expansion is most rapid. Proglacial lakes frequently develop from the enlargement and coalescence of one or more supraglacial lakes (Thakuri et al., 2016; Umesh et al., 2018). Proglacial- and supraglacial-lake development from 2008 to 2017 is significantly correlated with initial lake area in 2008 (R=0.82; Table A4); larger ice-contact proglacial lakes imply a larger water body in contact with the calving front of the glacier and more rapid retreat (Truffer and Motyka, 2016; King et al., 2019).

For the years before 2008, the year-round Landsat 5 TM data in many years do not fully cover the HMA region. In this study, we constructed the inventory over a 10-year time period. This is shorter than typical glacier response times, which start from a minimum of 10 years for short, steep glaciers to over 150 years for long, debris-covered glaciers (Scherler et al., 2011). Hence, lake expansion is not expected to be coupled with short-term climate trends, particularly for debris-covered glaciers (Umesh et al., 2018). In the inclusion of mass balance forcing of glacial-lake changes, the same questions about response times also occur. Hence, rather than focus on the short-term evolution of lake expansion, we investigated whether the climate and other factors have influenced the overall distribution of lake area, as observed in 2017.

To investigate the factors influencing the predominance of proglacial and supraglacial lakes, geomorphic, topographic, and climate parameters were correlated with lake area over a 1∘ × 1∘ grid, and these were aggregated (taking the mean or sum) for HMA regions. A statistically significant positive correlation exists between lake area and debris-covered glacial area (after Scherler et al., 2018) across HMA (R=0.36; Table A4), confirming the predominance of proglacial and supraglacial lakes forming on debris-covered glacier tongues (Nie et al., 2017). Correlations and significance levels strengthen if the Karakoram is excluded (Table A5). The Karakoram is known as an anomaly of positive glacier mass balances and glacier advances (Gardelle et al., 2012) and also has an anomalously small area of proglacial lakes. Glacier length (RGI-Consortium, 2017) and debris cover are strongly correlated (R=0.85; Table A4), reflecting abundant debris on most large, low-gradient valley glacier tongues in HMA; in turn, there is a statistically significant direct correlation between glacier length and lake area (R=0.32; Table A4) as these tongues provide the ideal conditions for the coalescence of supraglacial ponds and formation of large proglacial lakes (Nie et al., 2017; Richardson and Reynolds, 2000). Glaciers are generally longest and most heavily debris-covered in the Hindu Kush Himalaya region (Fig. 7a and b).

Some regions have comparable numbers of large debris-covered glaciers but substantial differences in total lake area and area growth rates (for example, the central Himalaya compared to central Tian Shan or western Pamir; Table A5). Regional differences in multi-decadal climate trends could play a role in this observation, with Nyainqêntanglha and the central and eastern Himalayan regions all being characterized by rapid warming and decreased precipitation since 1979 (Fig. 7c and d), favoring negative glacial mass balances (Brun et al., 2017). This plausibly explains why the lake area is typically larger in these regions relative to adjacent regions further to the west and north (e.g., the western Himalaya) despite often similar glacier characteristics (in terms of debris cover and glacier length) (Fig. 7e and f). Furthermore, there is very little debris-covered area but rapid warming in the eastern Himalaya, where proglacial lakes are abundant (Fig. 7f). These results emphasize that the distribution of supraglacial and proglacial lakes across HMA is primarily associated with the presence of large debris-covered glaciers, but regional variability in warming and precipitation trends over the past few decades has likely also had some influence (Shugar and Clague, 2011; Zhao et al., 2019; Umesh et al., 2018; Scherler et al., 2018). These results are consistent with previous findings at regional scales, which have demonstrated a rapid expansion of proglacial lakes on debris-covered glaciers, with expansion in the upstream direction demonstrated to occur primarily through a process of subsidence at the lake-contact debris-covered glacier tongue (Harrison et al., 2018; Song et al., 2016, 2017a).

We compared our dataset with that of X. Wang et al. (2020) for the closest period (2017 from the Hi-MAG database and 2018 from X. Wang et al., 2020) over the spatial extent of our HMA region. The differences in the total number and area of lakes between these two datasets are 6206 and 223.97 km2, respectively. We also found that 2077 glacial lakes with a total area of 178.77 km2 in our Hi-MAG dataset were not detected by X. Wang et al. (2020). The main reasons for the missed glacial lakes in Hi-MAG are the interference of some bad observations (cloud or snow), glacial lakes that have dried up or burst out, and glacial lakes that were located in the middle of the river.

To test the spatial correlation of the distributions of the glacial lakes in the two datasets, we compared the numbers of glacial lakes and their areas aggregated on a 0.1∘ × 0.1∘ grid for the HMA regions. The results for the total glacial-lake area, areas of glacial lakes larger than 0.04 km2, and the number of glacial lakes larger than 0.04 km2 are shown in Fig. A4. A clear and strong correlation can be observed for all the statistics between the Hi-MAG dataset and the glacial-lake data of X. Wang et al. (2020). Most of the points are distributed around the 1:1 line, which shows that there is great consistency between the two sets of data.

To quantitatively and systematically evaluate the accuracy of our data, we implemented stratified random sampling (Song et al., 2017b; Stehman, 2012), in which the glacial lakes were divided into four strata. The sample sizes were the spatial resolution (30 m) of the data, and the strata were designed such that C0W0 indicates that both the results are non-glacial lakes, C0W1 indicates a non-glacial lake in the present data and a glacial lake in X. Wang et al.'s (2020) data, C1W0 indicates a glacial lake in the present data and a non-glacial lake in X. Wang et al.'s (2020) data, and C1W1 indicates that both results are glacial lakes.

A total of 4000 points were randomly selected, as shown in Fig. A5. The number of samples for C1W1 and C1W0 were 1300 and 700, respectively, and these numbers have almost the same ratio as that between the total areas for the two strata (1450.50 vs. 732.77 km2). Because of the approximate total area with C1W0, we also randomly selected 700 samples from stratum C0W1. The remaining 1300 samples were from C0W0. Every validation sample was visually examined using Landsat imagery and higher-resolution imagery in Google Earth. Sample pixels were interpreted by a glacial-lake-mapping expert, and ambiguous samples were cross-validated by a second observer. If a sample was difficult to interpret, it was marked as ambiguous and excluded from the accuracy assessment. The sample number estimates were produced for each of the four strata (Table A6), and these strata totals were then summed to obtain the total accuracy.

For the 1300 samples that were considered by both datasets to be non-glacial lakes, after the pixel-by-pixel verification, 1215 were found to indeed be non-glacial lakes, while 37 were missed glacial lakes. In contrast, 1260 out of the 1300 samples belonged to the class of glacial lakes, and 25 were misclassified as glacial lakes by both inventories. A total of 307 error pixels were found in the results of X. Wang et al. (2020), constituting about half of the total validation number. For the glacial lakes identified only by our inventory, 678 out of 700 were correctly classified. Our results yielded high overall classification accuracy (88 %), user's accuracy (97 %), and producer's accuracy (82 %) for glacial-lake classification using Landsat data.

The Hi-MAG dataset was also compared with other Landsat-based lake inventories (Nie et al., 2017; Pekel et al., 2016; Zhang et al., 2015). The number of lakes in Hi-MAG was found to be 7268 higher, and the area was 644.26 km2 greater than the estimation for the Tibetan Plateau (Zhang et al., 2015). The largest discrepancies were found in the Gangdise, Himalaya, and Nyainqêntanglha mountains in 2010. Across the Himalaya region, we found 476.09 km2 of glacial lakes, 4.57 % more than previous estimates in 2015 (Nie et al., 2017). In addition, we qualitatively compared the lake extents between the publicly available high-resolution Global Surface Water (GSW) dataset (Pekel et al., 2016) and our Hi-MAG database summed by mountain range in 2015. For the sake of a reliable comparative analysis, lake polygons in the Hi-MAG dataset were converted into a grid format, and glacial lakes in the GSW were further extracted using the range of glacier buffer (10 km). Hi-MAG detected more glacial lakes in the Himalaya region, eastern Hindu Kush, and Tien Shan and fewer in eastern Pamir and western Kunlun Shan. Figure A6 illustrates the differences between our Hi-MAG glacial-lake results and the GSW-derived lake area for the whole HMA region.

The glacial-lake area observed in our lake dataset in the eastern Pamir and western Kunlun Mountains does not conform to the mapped surface water in the GSW for these sub-regions. While there are numerous glacial lakes from an open-water perspective, actually part of them are river segments. Additionally, the Himalaya, eastern Hindu Kush, and some other Tien Shan areas host thousands of glacial lakes that are not readily observable in the GSW dataset. Large discrepancies in mountainous glacial-lake estimates preclude a significant consistency between the GSW and our Hi-MAG lake data over the HMA region. The region with the highest consistency between GSW and Hi-MAG product is interior Tibet. There is little agreement for Tien Shan, where the weather is rainy and snowy in the region above 3000 m, and large quantities of ancient glacial deposits have accumulated. Here, glacial lakes are characterized by small sizes, and due to the influence of their source glaciers and lake beds as well as the water depth and sediment inflow, glacial lakes appear to have heterogeneous reflectance in the images. Errors could exist in datasets produced by automated classification, but, as noted, we also conducted detailed manual editing, so we were not relying exclusively on automatic classification. The Karakoram region seems to have fewer glacial lakes in our estimate, owing to the overestimation of surface water on debris-covered glaciers in the GSW dataset.

The low agreement between our Hi-MAG glacial-lake data and the GSW data is mainly due to their lack of systematic glacial-lake inventory and mapping capabilities. The lake dynamics and differing climate contexts within HMA may also lead to inconsistencies between the sub-regions. Hi-MAG might have made better use of the optimum satellite imaging season to map glacial lakes, potentially resulting in more complete mapping by avoiding conditions such as periods of lake ice that may confound mapping.

There are several important issues and limitations to the datasets produced and the methods used within this study that are important to highlight to potential users. (i) Bodies of water smaller than 9 connected pixels (e.g., 1×9 or 3×3 pixels, corresponding to 30×270 m or 90×90 m, respectively) and those obscured by frozen water surface or loose moraines or hidden by terrain shadows were not included. Broken floating ice or isolated moraines that stood in open water for some time were mapped. Supraglacial lakes such as melt ponds developed on the surface of glaciers present particular challenges because of their small size and highly dynamic properties. Most supraglacial lakes are transient or seasonal or at least fluctuate seasonally as they commonly drain and may refill. In fact, this short-duration seasonal water is in general more likely to be underestimated because of temporal discontinuities in the archive and gaps caused by persistent cloud cover. (ii) The spatial and temporal information reported in the Landsat dataset used in this study complements that acquired in the past. Nevertheless, the biggest limitations to glacial-lake mapping from these data are undoubtedly the geographic and temporal discontinuities of the Landsat archive itself. Historical data over the entire HMA before 2008 can be recovered partly from the Landsat 4 TM or Multispectral Scanner (MSS), Landsat 5 TM, and Landsat 7 ETM+ and partly from SPOT, other satellite systems, etc., although data access is not always at the full, free, and open level of Landsat. In this regard, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is freely accessible and has a higher resolution than Landsat, but its temporal coverage is very limited in most of HMA. Other Landsat-like moderate-resolution multi-spectral sources could also be used to improve and extend the temporal sampling. For example, the European Space Agency's Sentinel 2a satellite launched in 2015 and provides optical imagery at 10 m resolution (N. Wang et al., 2020; Yu et al., 2020), which will benefit future research combing all available satellite observations with GEE cloud-computing power and would make long-term monitoring of changes to HMA's glacial lakes and inland waters possible.