SPs

Browse all posts in the SPs category.

1 to 6 of 9

11APR 2022
SP3
SP3.1: Targetting snow drift and refreezing in glacier mass budgets with machine learning
Glacier surface mass balance (SMB) modelling has traditionally focused on improving the determination of surface energy balance conditions. However, water routing in the firn including refreezing, as well as the complex patterns of surface accumulation caused by snow drift, are poorly, or not at all, represented in most SMB models. Studies in the literature have often noted the uncertainties due to neglecting such mechanisms. This sub-project aims to improve the model representation of (i) the internal water budget and (ii) the snow drift contribution to the SMB using deep learning.Glacier surface mass balance (SMB) modelling has traditionally focused on improving the determination of surface energy balance conditions. However, water routing in the firn including refreezing, as well as the complex patterns of surface accumulation caused by snow drift, are poorly, or not at all, represented in most SMB models. Studies in the literature have often noted the uncertainties due to neglecting such mechanisms. This sub-project aims to improve the model representation of (i) the internal water budget and (ii) the snow drift contribution to the SMB using deep learning.
Read more about SP3.1: Targetting snow drift and refreezing in glacier mass budgets with machine learning
11APR 2022
SP2
SP2.1: Glacier outlines from optical and SAR imagery by deep learning
Glacier extent is an ECV and various international attempts exist to harmonize a global glacier inventory data set with outlines if possible from multiple data sources (GLIMS, IASC Randolph Glacier Inventory, RGI). However, to date no real operational large-scale repeat mapping capabilities exist. Glacier outlines are required at different time intervals e.g. with matching observational intervals for specific mass balance computations out of remote sensing measurements (e.g. for comparison with in-situ observations or on regional scale) or to validate ice dynamic modelling.Glacier extent is an ECV and various international attempts exist to harmonize a global glacier inventory data set with outlines if possible from multiple data sources (GLIMS, IASC Randolph Glacier Inventory, RGI). However, to date no real operational large-scale repeat mapping capabilities exist. Glacier outlines are required at different time intervals e.g. with matching observational intervals for specific mass balance computations out of remote sensing measurements (e.g. for comparison with in-situ observations or on regional scale) or to validate ice dynamic modelling.
Read more about SP2.1: Glacier outlines from optical and SAR imagery by deep learning
11APR 2022
SP3
SP3.2: Reconciling machine learning and glacier system modelling
Within this sub-project, we envisage to combine model-driven simulations of a coupled glacier system with data-driven machine learning techniques at specific benchmark sites in the Alps (e.g., Hintereisferner, Vernagtferner). A well-established ice-flow model will first be employed to produce observation-informed training and test data, which will then inform machine learning components that can replace such classical methods. The ultimate goal is to reduces computational efforts and prepare such hybrid techniques for long timescales and/or regional scales.Within this sub-project, we envisage to combine model-driven simulations of a coupled glacier system with data-driven machine learning techniques at specific benchmark sites in the Alps (e.g., Hintereisferner, Vernagtferner). A well-established ice-flow model will first be employed to produce observation-informed training and test data, which will then inform machine learning components that can replace such classical methods. The ultimate goal is to reduces computational efforts and prepare such hybrid techniques for long timescales and/or regional scales.
Read more about SP3.2: Reconciling machine learning and glacier system modelling
11APR 2022
SP3
SP3.3: Mass movement assessment and modelling in recent ice-free areas
The retreat of glacier ice fundamentally changes stability of the surrounding rock and debris slopes due to the massive thermal, mechanical and hydrological change initiating massive creep and rock slope failures (Haeberli et al., 2010). Alpine and arctic polythermal glaciers develop permafrost below the upper cold-based glaciers and unfrozen rock below the warm based lower glacier parts. Glacier retreat and warming cause high rates of permafrost degradation and sometimes aggradation, sudden changes in hydro- and cryostatic pressures and fast changes in lateral ice-stresses on slopes (Krautblatter & Leith, 2015). In SP3.3, we will develop generic mechanical models that undergo rapid thermal, mechanical and hydrological change.The retreat of glacier ice fundamentally changes stability of the surrounding rock and debris slopes due to the massive thermal, mechanical and hydrological change initiating massive creep and rock slope failures (Haeberli et al., 2010). Alpine and arctic polythermal glaciers develop permafrost below the upper cold-based glaciers and unfrozen rock below the warm based lower glacier parts. Glacier retreat and warming cause high rates of permafrost degradation and sometimes aggradation, sudden changes in hydro- and cryostatic pressures and fast changes in lateral ice-stresses on slopes (Krautblatter & Leith, 2015). In SP3.3, we will develop generic mechanical models that undergo rapid thermal, mechanical and hydrological change.
Read more about SP3.3: Mass movement assessment and modelling in recent ice-free areas
11APR 2022
SP2
SP2.2: Radar penetration of TanDEM-X on glaciers & ice caps SAR tomography for 3D imaging of snow and firn structures
The radar signal can penetrate into snow, firn, and ice bodies depending on liquid water content and internal structure of the porous medium as well as radar imaging parameters. Consequently, the interferometric phase center of the backscattered radar signal is located at a certain depth below the actual topographic surface, leading to a bias between the glacier surface and the estimated one from SAR interferometry (Rott et al 2021, Rizzoli et al 2017). Radar penetration is still a large source of uncertainty in estimates of glacier volume and mass change from bi-static SAR mission data like bi-static TanDEM-X or the upcoming HRWS MirrorSAR (Braun et al. 2019, Huber et al. 2020).The radar signal can penetrate into snow, firn, and ice bodies depending on liquid water content and internal structure of the porous medium as well as radar imaging parameters. Consequently, the interferometric phase center of the backscattered radar signal is located at a certain depth below the actual topographic surface, leading to a bias between the glacier surface and the estimated one from SAR interferometry (Rott et al 2021, Rizzoli et al 2017). Radar penetration is still a large source of uncertainty in estimates of glacier volume and mass change from bi-static SAR mission data like bi-static TanDEM-X or the upcoming HRWS MirrorSAR (Braun et al. 2019, Huber et al. 2020).
Read more about SP2.2: Radar penetration of TanDEM-X on glaciers & ice caps SAR tomography for 3D imaging of snow and firn structures
11APR 2022
SP2
SP2.3: Improved volume to mass conversion
Glacier mass balance variations are an essential indicator for regional and global climate fluctuations. Today, glacier volume changes can be detected with satellite based systems to a reasonable degree of accuracy, while density estimates of the affected volume are still based on models and a priori assumptions. Even though, methods for volume to mass conversion are established, there exists a considerable knowledge gap with respect to validation and temporal stability of the assumptions. Especially the transient evolution of the firn pack will strongly influence the density assumptions of the glaciers with time.Glacier mass balance variations are an essential indicator for regional and global climate fluctuations. Today, glacier volume changes can be detected with satellite based systems to a reasonable degree of accuracy, while density estimates of the affected volume are still based on models and a priori assumptions. Even though, methods for volume to mass conversion are established, there exists a considerable knowledge gap with respect to validation and temporal stability of the assumptions. Especially the transient evolution of the firn pack will strongly influence the density assumptions of the glaciers with time.
Read more about SP2.3: Improved volume to mass conversion