This dataset focuses on glacier mass change in the Eastern Tibetan Plateau's (ETPR) source rivers from the 1970s to 2000. It provides data at two resolutions: high-resolution glacier-level data (30 m) and lower-resolution gridded data (0.1 and 0.5 degrees). The high-resolution data offers valuable insights for individual glaciers, including elevation change over 30 years, annual mass balance, and associated uncertainty for 13,117 glaciers. To calculate mass change, we compared digital elevation models (DEMs) derived from 1970s topographic maps (aerial photogrammetry) with the Shuttle Radar Topography Mission (SRTM) data from 2000. Co-registration, bias correction, and quality control procedures ensured data accuracy. We evaluated the DEM-based elevation differences using ICESat-2 and KH-9 data. The results demonstrate good quality, particularly at lower altitudes, similar to KH-9 data. However, high-altitude results have higher uncertainty due to limitations in capturing highly reflective snow surfaces on steep terrain using aerial photos. This dataset is a valuable resource as it covers 72% of ETPR glaciers. This comprehensiveness makes it ideal for calibrating parameters in mass balance simulations at various scales. Furthermore, the dataset can be used to assess changes in glacier hydrological effects before and after 2000.
We employed a total of 718 historical topographic maps including 142 at a scale of 1:50,000 and 576 at a scale of 1:100,000 compiled from aerial photos taken from 1957 to 1983 by the Chinese Military Geodetic Service. In this study, we use SRTM DEM (no void filled version) with a resolution of 1 arc second (~30 m) refer to the glacier surface in the year of 1999 suggested by previous studies (e.g., Gardelle et al., 2013; Mcnabb et al., 2019). In this study, we acquired the KH-9 images covering Xixiabangma Mountain，and ICESat-2 data.
To generate the grid product, we took the following steps: 1) Firstly, we spatially merged the elevation differences, elevation errors, and timestamps of all effective glaciers. 2) For each grid cell, we calculate the average glacier elevation difference, elevation error, and timestamp within that grid cell. These averages are designated as grid cell values. 3) For each grid cell, we calculate the NMAD and pixel count of the height difference. Calculate the grid scale resampling error using formula (6). 4) Combining the grid scale height difference and timestamp, apply formula (7) to calculate the grid scale mass balance. 5) Combining the grid scale elevation variation error and timestamp, apply formula (8) to calculate the initial grid scale mass balance error. Finally, the final mass balance error is obtained by combining the resampling error.
The data quality is good.
| # | number | name | type |
| 1 | 41991234 | National Natural Science Foundation of China |
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CSTR:11738.11.NCDC.NIEER.DB6680.2024
10.12072/ncdc.nieer.db6680.2024
