Glacier surge monitoring from temporally dense elevation time series: application to an ASTER dataset over the Karakoram region

Abstract. Glacier surges are spectacular events that lead to surface elevation changes of tens of meter in a period of a few months to a few years, with different patterns of mass transport. Existing methods of elevation change estimate of surges, and subsequent quantification of their mass transported, rely on differencing pairs of digital elevation models (DEMs) that may not be acquired regularly in time. In this study, we propose a workflow to filter and interpolate a dense time series of DEMs specifically for the study of surge events. We test this workflow on a global 20-year dataset of DEMs from the optical satellite sensor Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The multi-step procedure includes linear non-parametric Locally Weighted Regression and Smoothing Scatterplots (LOWESS) filtering and Approximation by Localized Penalized Splines (ALPS) interpolation. We run the workflow over the Karakoram mountain range (High Mountain Asia). We compare the produced dataset to previous studies for four selected surge events (surges of Hispar, Khurdopin, Kyagar and Yazghil glaciers). We demonstrate that our workflow captures thickness changes at monthly scale with detailed patterns of mass transportation. Such patterns includes surge front propagation, changes in dynamic balance line, and slow surge onset among others, and allows an unprecedentedly detailed description of glacier surges at the scale of a large region. The workflow preserves most of the elevation change signal, with underestimation or smoothing in a limited number of surge cases.