Glaciers are among the best indicators of terrestrial climate variability, contribute importantly to water resources in many mountainous regions and are a major contributor to global sea level rise. In the Hindu Kush-Karakoram-Himalaya region (HKKH), a paucity of appropriate glacier data has prevented a comprehensive assessment of current regional mass balance. There is, however, indirect evidence of a complex pattern of glacial responses in reaction to heterogeneous climate change signals. Here we use satellite laser altimetry and a global elevation model to show widespread glacier wastage in the eastern, central and south-western parts of the HKKH during 2003-08. Maximal regional thinning rates were 0.66 ± 0.09 metres per year in the Jammu-Kashmir region. Conversely, in the Karakoram, glaciers thinned only slightly by a few centimetres per year. Contrary to expectations, regionally averaged thinning rates under debris-mantled ice were similar to those of clean ice despite insulation by debris covers. The 2003-08 specific mass balance for our entire HKKH study region was -0.21 ± 0.05 m yr(-1) water equivalent, significantly less negative than the estimated global average for glaciers and ice caps. This difference is mainly an effect of the balanced glacier mass budget in the Karakoram. The HKKH sea level contribution amounts to one per cent of the present-day sea level rise. Our 2003-08 mass budget of -12.8 ± 3.5 gigatonnes (Gt) per year is more negative than recent satellite-gravimetry-based estimates of -5 ± 3 Gt yr(-1) over 2003-10 (ref. 12). For the mountain catchments of the Indus and Ganges basins, the glacier imbalance contributed about 3.5% and about 2.0%, respectively, to the annual average river discharge, and up to 10% for the Upper Indus basin.

Abstract.There are an increasing number of digital elevation models (DEMs) available worldwide for deriving elevation differences over time, including vertical changes on glaciers. Most of these DEMs are heavily post-processed or merged, so that physical error modelling becomes difficult and statistical error modelling is required instead. We propose a three-step methodological framework for assessing and correcting DEMs to quantify glacier elevation changes: (i) remove DEM shifts, (ii) check for elevation-dependent biases, and (iii) check for higher-order, sensor-specific biases. A simple, analytic and robust method to co-register elevation data is presented in regions where stable terrain is either plentiful (case study New Zealand) or limited (case study Svalbard). The method is demonstrated using the three global elevation data sets available to date, SRTM, ICESat and the ASTER GDEM, and with automatically generated DEMs from satellite stereo instruments of ASTER and SPOT5-HRS. After 3-D co-registration, significant biases related to elevation were found in some of the stereoscopic DEMs. Biases related to the satellite acquisition geometry (along/cross track) were detected at two frequencies in the automatically generated ASTER DEMs. The higher frequency bias seems to be related to satellite jitter, most apparent in the backlooking pass of the satellite. The origins of the more significant lower frequency bias is uncertain. ICESat-derived elevations are found to be the most consistent globally available elevation data set available so far. Before performing regional-scale glacier elevation change studies or mosaicking DEMs from multiple individual tiles (e.g. ASTER GDEM), we recommend to co-register all elevation data to ICESat as a global vertical reference system.

ABSTRACT. Deriving glacier outlines from satellite data has become increasingly popular in the past decade. In particular when glacier outlines are used as a base for change assessment, it is important to know how accurate they are. Calculating the accuracy correctly is challenging, as appropriate reference data (e.g. from higher-resolution sensors) are seldom available. Moreover, after the required manual correction of the raw outlines (e.g. for debris cover), such a comparison would only reveal the accuracy of the analyst rather than of the algorithm applied. Here we compare outlines for clean and debriscovered glaciers, as derived from single and multiple digitizing by different or the same analysts on very high-(1 m) and medium-resolution (30 m) remote-sensing data, against each other and to glacier outlines derived from automated classification of Landsat Thematic Mapper data. Results show a high variability in the interpretation of debris-covered glacier parts, largely independent of the spatial resolution (area differences were up to 30%), and an overall good agreement for clean ice with sufficient contrast to the surrounding terrain (differences $5%). The differences of the automatically derived outlines from a reference value are as small as the standard deviation of the manual digitizations from several analysts. Based on these results, we conclude that automated mapping of clean ice is preferable to manual digitization and recommend using the latter method only for required corrections of incorrectly mapped glacier parts (e.g. debris cover, shadow).

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Glacier-wide mass balance has been measured for more than sixty years and is widely used as an indicator of climate change and to assess the glacier contribution to runoff and sea level rise. Until recently, comprehensive uncertainty assessments have rarely been carried out and mass balance data have often been applied using rough error estimation or without consideration of errors. In this study, we propose a framework for reanalysing glacier mass balance series that includes conceptual and statistical toolsets for assessment of random and systematic errors, as well as for validation and calibration (if necessary) of the glaciological with the geodetic balance results. We demonstrate the usefulness and limitations of the proposed scheme, drawing on an analysis that comprises over 50 recording periods for a dozen glaciers, and we make recommendations to investigators and users of glacier mass balance data. Reanalysing glacier mass balance series needs to become a standard procedure for every monitoring programme to improve data quality, including reliable uncertainty estimates