Abstract
Variability in snow cover strongly influences mass budgets of glaciers, permafrost distribution, and seasonal discharge of rivers. In times of a changing climate, the spatio-Temporal patterns of snow cover are of high interest. In this study, snow cover time series for the Aksu catchment in Central Tien Shan have been generated from optical remote sensing imagery. The analyses span a period between 1986 and 2013 and imbed Advanced Very High Resolution Radiometer (AVHRR) level 1b scenes, which were classified using a dichotomous decision scheme, as well as the preprocessed Moderate Resolution Imaging Spectrometer (MODIS) snow cover product. High congruence of the results could be achieved in spite of different sensors involved. However, a small bias appears especially at high elevations. The results from 2000 to 2013 reveal that snow accumulation begins in October and melting starts in March. Above an elevation of around 5200 m a.s.l., permanent snow cover can be expected, which is mirrored by a zonal mean of more than 85% of snow for the whole period 1986-2013. Anomalies are very indicative and reveal a high interannual variability of snow cover in terms of quantity and spatial distribution. Change detection of snow cover probability (SCP) shows a slight decrease in lower altitudes up to 4000 m a.s.l. and an opposite trend above. However, the negative trends are not significant. Significant gradients have been found only at high elevations where the two data sources could not perfectly be harmonized. Comparisons with climatic variables show a similar temporal behavior of SCP and temperatures.
Original language | English |
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Article number | 7293625 |
Pages (from-to) | 5361-5375 |
Number of pages | 15 |
Journal | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Volume | 8 |
Issue number | 11 |
DOIs | |
Publication status | Published - 1 Nov 2015 |
Keywords
- Advanced Very High Resolution Radiometer (AVHRR)
- Moderate Resolution Imaging Spectrometer (MODIS)
- snow
- Tien Shan