Impacts of Climate and Supraglacial Lakes on the Surface Velocity of Baltoro Glacier from 1992 to 2017

Abstract

The Baltoro Glacier is one of the largest glaciers in the Karakoram mountain range. Long-term monitoring of glacier dynamics provides key information on glacier evolution in a changing climate, which is essential for regional water resource and natural hazard management. On large glaciers, detailed field based mass balance is not feasible. Ice dynamic variations quantify changes in mass transport and possibly the influence of environmental parameters on the evolution of the glacier. Although velocity variations of Baltoro Glacier during winter and summer are linked to seasonally enhanced basal sliding, little is known about differences in timing and magnitude of (intra-)seasonal velocity variations and their determining mechanisms. We present time series of annual, seasonal, and intra-seasonal glacier surface velocities by means of intensity offset tracking applied on multi-mission Synthetic Aperture Radar (SAR) data for a period of 25 years from 1992 to 2017. Supraglacial lakes forming on the downstream glacier surface in summer were mapped from 1991 to 2017 based on the Normalized Difference Water Index (NDWI), calculated from multi-spectral Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery. Additionally, precipitation data of the Tropical Rainfall Measurement Mission (TRMM) and temperature data of ERA-Interim were used to derive the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) from 1998 to 2017. Linking surface velocities to the SPI confirmed a strong correlation between heavy precipitation events in winter and the magnitude and the timing of glacier acceleration in summer. Downstream extensions of summer acceleration that have been found since 2015 may be explained by additional water draining from an increased number of supraglacial lakes through crevasses that have been formed in consequence of higher initial velocities, evoked by strong winter precipitation. The warmer melt seasons observed in the years 2015 to 2017 additionally affects the formation of a supraglacial lake, so stronger summer acceleration events in recent years may be indirectly related to global warming.

Global time series and temporal mosaics of glacier surface velocities, derived from Sentinel-1 data

“…In order to facilitate oversampling during tracking, the bursts of the master image of each processing pair are corrected for their azimuth phase ramps (de-ramping) and the derived correction function is then applied to the bursts of the slave scene (Miranda 2017;Wegmüller et al 2016). After de-ramping, the bursts are mosaicked and a well-established intensity offset tracking algorithm implemented in the GAMMA software package is applied, which uses a moving window approach to determine normalized cross correlation peaks between patches of the master and the slave intensity image in order to derive azimuth and slant range displacement (Strozzi et al 2002;Wegmüller et al 2016;Wendleder et al 2018;Seehaus et al 2018). The technique is based on tracking persistent patterns of intensity values in both images, which are either formed by surface features such as crevasses (feature tracking) or correlated radar speckle (speckle tracking).…”

Modeling Surface Processes on Debris-Covered Glaciers: A Review with Reference to the High Mountain Asia

“…Dehecq et al [92] delivered glacier surface velocity fields by applying feature tracking to Landsat-7 image pairs using JPL auto-RIFT program [93]. Wendleder et al [94] presented glacier surface velocities at various time series using intensity offset tracking applied to the Synthetic Aperture Radar (SAR) data from 1992 to 2017.…”

Tracking Lake and Reservoir Changes in the Nenjiang Watershed, Northeast China: Patterns, Trends, and Drivers

“…where Green and NIR represent the reflectance of the green and Near Infrared (NIR) bands, respectively [25]. The data processing steps for the lake and reservoir inventory are shown in Figure 3.…”