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For this exercise, the imagery provided for download includes only bands 4,5 and 10. Import these bands using the Landsat import with options tool using band 10 for red. You will notice the imagery has already been cut to make a smaller file for download however for this exercise you will need to clip this image again to one large burnt area for mapping as shown to the right.
Here we analyse multi-temporal geodetic glacier mass budgets in seven climatically different glacierised regions over a timespan of almost six decades to characterize glacier mass budget variability across HMA (Fig. 1). These geodetic mass balance estimates have been derived from DEMs generated from available satellite data including Corona KH-4, Hexagon KH-9 and more recent spaceborne data including high-resolution Pléiades imagery (Supplementary Note 1 and Supplementary Table 1). Additionally, we examine influential climate drivers in different regions using ERA5 Land and in-situ weather station data to improve our understanding of the response of HMA glaciers to a changing climate. Our results reveal accelerated mass loss over the seven study areas, even in the regions with previously balanced mass budgets, due to widespread increases in summer temperature.
To better understand how climate and climate change drives HMA glacier mass balance we characterised the general climate in the seven study regions and compared our records of mass changes to surface fluxes from a regional model (ERA5 Land) forced with reanalysis data38. The regions Northern Tien Shan, Ak-Shirak (Central Tien Shan) and Muztagh Ata (Eastern Pamir) are mainly influenced by mid-latitude westerlies. While the former is more humid the latter two regions are relatively dry (Supplementary Note 2). The regions Purogangri Ice Cap and Western Nyainqentanglha on the Tibetan Plateau are located in the drier transitional zone between the mid-latitude westerlies and monsoon influenced regions. The Poiqu/Langtang and Gurla Mandhata regions are located in the humid Central Himalaya, where both the Indian and South-East Asian summer monsoon govern meteorological conditions.
We extracted temperature and precipitation estimates from the ERA5 Land grid cells representing glacierised elevations for each sub region of our study (Supplementary Table 17) and specifically considered summer air temperature (SumT) and solid precipitation (snow fall, SolP: considering any precipitation corresponding to temperature
The generally weaker agreement of precipitation records (Table 1) likely reflects the relatively sparse network of weather stations and their typical location, aside from Tuyuksu (Northern Tien Shan), at lower elevations than glacierised terrain. Thus, in-situ measurements may not be fully representative of precipitation received by glaciers. In combination with the well documented difficulties associated with recording precipitation, high elevation meteorological stations may be more biased than those at lower elevations41,42. On the other hand, the ERA5 Land reanalysis product is obtained by combining model data with observations considering the laws of physics to produce a globally consistent dataset43. To allow for the comparison of our mass balance data against a spatially and methodologically homogenous record of meteorological variables, we conduct further analyses considering the ERA5 Land dataset primarily, but also provide a comparison of our results to meteorological station records (Table 1).
Utilized Corona KH-4, Hexagon KH-9, and ASTER data are available at www.earthexplorer.usgs.gov. Pléiades, SPOT, TerraSAR-X, and GeoEye are commercial. ERA5 land data is available from !/dataset/reanalysis-era5-land?tab=form. Weather station data is restricted. Geodetic mass balances for each glacier grids of surface elevation changes will be available from PANGAEA ( ) and also available for download at www.mountcryo.org and upon request from the authors. All other relevant non-restricted data are available from the authors upon request. 2ff7e9595c
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