SP3.1: Targetting snow drift and refreezing in glacier mass budgets with machine learning

Subproject 3.1

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.
This aim links ideally with ongoing snowdrift measurements on Hintereisferner in the frame of a running joint project between FAU and the University of Innsbruck (http://schism-project.info/). It furthermore links very well with our strong background in SMB modelling and developing process-based model parameterizations. We hypothesize, however, that the accuracy of such parameterizations can be increased drastically by including machine learning algorithms and new measurements on snow drift dynamics.

In this subproject, we will explore the associated added value by sensitivity simulations and analyses of the resultant mass and energy balance components. An improved knowledge of the mechanisms in question is also important for the precise estimation of sea level contributions from glaciers measured from altimetry and DEM differencing (SP2.2 and 2.3), since the measured elevation changes have to be converted to mass using a certain density assumption. The SP also links to SP1.2 and SP1.3 where internal structures of the snow and firn cover are mapped.

For specific information on the sub-project please contact: Prof. Dr. Thomas Mölg, Institut für Geographie, Wetterkreuz 15, 91058 Erlangen, T: 09131-85-22633, thomas.moelg@fau.de, https://www.geographie.nat.fau.de/

Co-PIs: L. Nicholson, R. Prinz (Univ. Innsbruck), R. Hock (Univ. Oslo)

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Manuel Saigger

Manuel Saigger


My name is Manuel Saigger and within the IDP MOCCA I will be working on the influence of snow drift and internal refreezing on the glacier mass balance. Before I started working in Erlangen,  I studied atmospheric sciences at the University Innsbruck. There I mainly focused on weather forecasting and weather simulations in alpine terrain and got really fascinated by the complex nature of flow fields in the surrounding of mountains. These complex wind patterns can in return lead to very complex patterns in snow accumulation, for example by redistribution of the snow.

Here my project comes into play. Because despite possibly being very important, this redistribution is not reflected in most glacier mass balance models, which on the one hand creates uncertainty in these models, but on the other hand gives us the opportunity to work on this problem and learn more about these fascinating processes.


SP3.2: Reconciling machine learning and glacier system modelling

Subproject 3.2

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.


Detailed Information and position requirements of this SP are formulated in this PDF.

For specific information on the sub-project please contact:
Dr. Johannes Fürst (NGL)
Department of Geography and Geosciences, Institute of Geography
Wetterkreuz 15, 91058 Erlangen, T: 09131-85-26680, Johannes.fuerst@fau.de
Prof. Dr. Harald Köstler
Department of Computer Sciences, System Simulation
Cauerstraße 11, 91058 Erlangen, T: 09131-85-29359, harald.koestler@fau.de

I. Tabone (FAU Geography)
O. Galiardini (Univ. Grenoble)
F. Maussion (Univ. Innsbruck)

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Mamta KC

Mamta KC


After graduating with a master’s degree in Environmental and Natural Resources from Kathmandu University, Nepal, I opted for the second master’s program in Climate and Environmental Science at the FAU Institute of Geography to broaden my horizon and enhance my environmental data analysis knowledge. During my master’s program, my interest in glacier environment, climatology, and machine learning grew.

The potential of deep learning applications to understand real-world physical phenomena captured my attention. Using efficient deep learning-based models to learn physics-based glacier models is a promising field of research. Thus, I look forward to incorporating machine learning into glacier modeling during my Ph.D. in the IDP M3OCCA program. My research will focus on developing physics-based deep learning emulators that will learn to predict the glacier dynamics. The emulators will be trained using the simulated data generated from the different glacier-based physical models.


SP3.3: Mass movement assessment and modelling in recent ice-free areas

Subproject 3.3

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. Release of ice stress and thermal conditions will be refined by additional information on glacier retreat (SP2.1) and on simulated basal ice temperatures (SP3.2). For the latter temperatures, SP3.3 will build on the finite-element Elmer/Ice applications calibrated to the benchmark glaciers for the last decades (SP3.2). Here, these simulations will be extended to cover the last century.

The presumable starting date is the end of the Little Ice Age (LIA). The modelling strategy is an initial equilibration run under LIA conditions followed by a forward simulation to present day forced by climatic data (e.g. ERA-20C, ERA5, CORDEX-CORE) downscaled to local AWS measurements (joint effort with SP3.1).
The centennial scope assures that the long-term memory is imprinted in the 3D temperature field. Accounting for both retreat and temperature information, we will develop generic rock-ice-mechanical models using the benchmark sites Vernagtferner and Hintereisferner.
The benchmark model will apply temperature-dependent changes in rock stability, ice stability in fractures and rock-ice interfaces as well as changes in hydro- and cryostatic forcing and lateral destabilisation due to reducing glacier support (Krautblatter et al. 2013).
Model development will occur on basis of discontinuum mechanical (UDEC) models that have been developed in recent research projects (Mamot et al., 2020). The generic mechanical models will be clustered to specific conditions indicated by glacier retreat and elevation change information outside glaciers to constrain models that anticipate the propensity of major hazards due to rock slope and debris slope failures in the recently ice-free slope and glacier forefields.


For specific information on the sub-project please contact: Prof. Dr. Michael Krautblatter, Ingenieurfakultät Bau, Geo & Umwelt, Arcisstr. 21, 80333 Munich, T: 089-289-25866, m.krautblatter@tum.de, http://www.landslides.geo.tum.de

Co-PIs: J. Fürst (FAU Geography), B. Etzelmüller, S. Westermann (Univ. Oslo)

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Felix Pfluger

Felix Pfluger


I am Felix. My research focuses on understanding the interaction between glaciers and permafrost in alpine environments. During paraglacial transition phases, as the landscape changes from ice to ice-free states, geomorphic activity is higher than usual. The relatively rapid and drastic change in thermal and hydrological characteristics predisposes rock slopes to a more fragile state. Furthermore, the evolution of permafrost in space is critical for the assessment of rock slope stability and potential hazards.


My aim is to develop a rock-ice mechanical model suitable to predict rock mass failure concomitant with changing cryospheric conditions. Besides rock mechanical laboratory experiments I will take advantage of the capabilities of the MOCCA group by having a joint field campaign using geophysics to explore glacial and permafrost conditions at a study site in Kaunertal. The knowledge of seismic, geoelectric and radar measurements will complement the permanent temperature monitoring of rock surface temperature to gain insights into the thermal characteristics at the study site. By coupling the mechanical model to a dynamic thermal model, we will be able to assess and predict rock mass failures in time.


Besides scientific goals you can hang out with me to blues rock music, even better to play some music together, to do some rock climbing or go fishing. As growing up in the Alps I am happy to work on a local study site in my home region.