Despite its significance, the snow water equivalent (SWE) is poorly characterized in many mountain regions due to i) a lack of in situ measurements and ii) the difficulty to measure the SWE directly from satellite observations. 

We developed a tool to simulate the spatial distribution of SWE at high resolution (typically 100 m) in any region of interest using SnowModel (Liston and Elder, 2006a, 2006b), global meteorological data (ERA5) and satellite observations of the snow cover fraction (SCF). Satellite observations are used to mitigate errors in the model parameterization and the meteorological forcings using the particle batch smoother  (Margulis et al., 2015). This method consists in computing N perturbed simulations on the whole hydrological season and transforming the simulated SWE in a simulated SCF. In this study, we used the formula linking the SWE and the SCF of the Noah Land Surface Model as the measurement operator. After that, the simulations are compared to remotely sensed SCF data and weighted according to their agreements with the observations. 

We implemented this data assimilation method with both MODIS and Sentinel-2 SCF data. We are testing it on the Bassies catchment in the French Pyrenees, and on the Tuolumne River Catchment in the Sierra Nevada, USA. In the Bassies catchment, we compare the results with snow depth maps from Pléiade satellites. We perturbed the precipitations with a log-normal law and found a very good agreement between the posterior simulated SCF and the observed one in Bassies as expected. However, the simulated snow depths in this catchment do not match the Pléiades snow depths observations. We added the perturbation of the air temperature with a normal law and found similar results. We also found a very small sensitivity of the posterior snow depth on the empirical parameters of the measurement operator. These results suggest that the SWE-SCF relationship may not be sufficiently informative in the study site of Bassiès.  We will present the extension of this work to the Tuolumne river basin where the Airborne Observatory provides SWE maps over a larger region.

How to cite: Sourp, L., Gascoin, S., Pedinotti, V., Jarlan, L., and Alonso-González, E.: Evaluation of a tool to simulate high resolution mountain SWE from global datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7804, https://doi.org/10.5194/egusphere-egu24-7804, 2024.

Mapping the dynamics of snow water equivalent (SWE) is critical for understanding the hydrology of mountain regions. While methods to reconstruct SWE exist, they usually rely on either remote sensing data or the presence of in-situ observations. Streamflow observations can be considered an indirect and delayed observation of catchment-wide SWE, but despite their broad spatial and temporal availability, their potential for SWE reconstructions has not been explored so far.

In this study, we investigate how much SWE information can be extracted from the streamflow record, both in terms of its total mass as well as its spatial distribution. To this end, we set up an inverse streamflow-based SWE reconstruction framework that can operate without remote sensing data or in-situ snow observations. As a basis, we use a distributed hydrological model with a temperature-index snow model at 1km resolution, which generates SWE reconstructions from a set of prior snow and climate parameters and translates them into streamflow. By using the streamflow observations to optimize these parameters, we obtain SWE reconstructions that better match the streamflow.

However, there is likely a multitude of SWE reconstructions that all lead to the same streamflow, which is defined as an ill-posed inverse problem. In order to find out by how much the streamflow can constrain the prior SWE reconstructions, we perform an experiment with synthetic observations. Using synthetic observations instead of real observations eliminates both model and observation errors, allowing us to focus solely on the nature of this ill-posed problem.

Firstly, we select a set of soil, snow and climate parameters to generate synthetic SWE observations and their corresponding streamflow. All parameters are kept constant over the entire period except the parameter controlling the snowfall bias correction, which is set to fluctuate on a yearly basis and consequently also needs to be optimized for each year separately. Then, we run a single calibration to attempt to rederive these parameters and reconstruct the synthetic observations. Finally, we analyze our posterior ensemble of parameter sets and SWE reconstructions and quantify the resemblance to the synthetic streamflow and SWE observations. We expect to find a near-perfect match with the synthetic streamflow observations, a strong constraint of the total catchment-wide SWE but a weak constraint of the spatial distribution of this SWE.

How to cite: Wiersma, P. and Mariéthoz, G.: Inverse hydrological modeling to infer historical snow mass based on streamflow records, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20649, https://doi.org/10.5194/egusphere-egu24-20649, 2024.

The subglacial discharge of sediment-rich meltwater plumes can be detected in satellite imagery where plumes reach the ocean surface at the terminus of tidewater glaciers. These meltwater plumes can influence glacier melting and ocean properties in important ways. Studying them provides insights into meltwater pathways in glacial hydrological systems.

Sediment-rich meltwater plumes are observed extensively in Greenland, Svalbard and other regions, however in Antarctica observations have been more limited. Recent detections of seasonal speed variations on tidewater glaciers of the Antarctic Peninsula suggest that surface meltwater reaching the glacier bed may be an important factor influencing ice dynamic behaviour on the Antarctic Peninsula Ice Sheet (APIS), however surface-bed hydrological connections have not been directly observed through fieldwork on the mainland APIS. Therefore, studying the presence and distribution of sediment plumes around the Peninsula can provide insights to understand the factors influencing newly observed seasonal ice speed fluctuations.

Here we develop a remote-sensing approach to map the locations and frequency of sediment plumes on the Antarctic Peninsula coastline using high-resolution multi-spectral imaging from Sentinel-2 satellites and a U-Net based convolutional neural network. This methodology allows us to detect small sediment plumes in images with high cloud and sea-ice densities. We apply our approach to the Antarctic Peninsula north of 65°S, including the South Shetland Islands, to produce a time-series of sediment plumes from 2016 to 2023 covering 150,000 km².

We use these results combined with outputs from regional climate models and reanalysis to assess the link between surface-visible sediment plumes and surface melt and runoff from the Antarctic Peninsula’s glaciers. We find that the timings and locations of sediment plumes correspond to modelled runoff, providing evidence for widespread surface-bed hydrological connections in the Antarctic Peninsula.

How to cite: Wallis, B., Hogg, A., and Hogg, D.: Mapping glacial sediment plumes in the Antarctic Peninsula using deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4036, https://doi.org/10.5194/egusphere-egu24-4036, 2024.

Predicting the future contribution of the ice sheets to sea level presents several challenges due to the lack of observations of critical boundary conditions, such as basal sliding. Traditional numerical models often rely on data assimilation methods to determine spatially variable friction coefficients by solving an inverse problem, given an empirical friction law. However, these approaches are not versatile, as they demand sometimes extensive code development efforts when integrating new physics into the model. Furthermore, the requirement for comprehensive data alignment on the computational grid hampers their adaptability, especially in handling sparse data effectively. To tackle these challenges, we propose a transformative approach utilizing Physics-Informed Neural Networks (PINNs) to seamlessly integrate observational data and underlying physical laws into a unified loss function, facilitating the solution of both forward and inverse problems within the same framework. We illustrate the versatility of PINNs by applying the framework to two-dimensional problems on Helheim Glacier in southeast Greenland. By systematically concealing one component within the system, we showcase the ability of PINNs to accurately predict and reconstruct hidden information, emphasizing their adaptability to handle scenarios marked by missing or incomplete datasets. Furthermore, we extend the application to address a challenging mixed inversion problem. We show how PINNs are capable of inferring the basal friction coefficient while simultaneously filling gaps in sparsely observed ice thickness. This mixed inversion problem represents a class of scenarios beyond the reach of conventional numerical methods. Our unified framework offers a promising avenue to enhance the predictive capabilities of ice sheet models, reducing uncertainties and advancing our understanding of intricate ice dynamics.

How to cite: Cheng, G., Morlighem, M., and Francis, S.: A Unified Framework for Forward and Inverse Modeling of Ice Sheet Flow using Physics Informed Neural Networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2495, https://doi.org/10.5194/egusphere-egu24-2495, 2024.

The thickness of englacial isochrones is the combined product of accumulation and dynamical thinning by the flow of ice.Dated ice stratigraphy data provides access to this archive , which in principle holds the potential for improving simulations and reconstructions  of the Greenland ice sheet. However, the combined effects of accumulation and dynamical thinning are convoluted and spatially heterogeneous, making it difficult to separate their respective contributions to the observed thickness of isochrones.

Here we simulate isochrones explicitly using the Englacial Layer Simulation Architecture (ELSA) coupled with a fully transient thermomechanical ice sheet model. This enables us to link accumulation rates to the ice stratigraphy, including a physically consistent representation of dynamical thinning. By ensemble data assimilation techniques, the linear model response to changes in past local accumulation are estimated, enabling the inversion of the model and reconstruction of accumulation rates from stratigraphy. An iterative approach is chosen to account for the nonlinear response of ice flow to anomalous accumulation. The result is an optimized simulation of the Greenland ice sheet with an englacial stratigraphy matching the observations, forced by the reconstructed accumulation. Because the model is fully transient including ice dynamics, our approach also constrains ice sheet stability and sea level contribution.

Here we present preliminary findings from inversions of idealized ice sheet stratigraphy, including some encouraging insights, physical and methodological limitations and challenges yet to be overcome.

How to cite: Voigt, P. I. and Born, A.: Assimilating the Greenland ice sheet stratigraphy for reconstructing accumulation rates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9687, https://doi.org/10.5194/egusphere-egu24-9687, 2024.

The accurate quantification of glacier mass balance is of vital importance for the evaluation of climate change impact and the management of hydrological resources. However, traditional modeling methodologies on a regional scale are frequently plagued by uncertainties in forcing data, model structure, and parameters. Data assimilation emerges as an effective technique to incorporate observations into modeling, thereby reducing the uncertainty of results. In this study, we evaluate the performance of different ensemble-based schemes, including the Ensemble Smoother (ES) and the Ensemble Smoother-Multiple Data Assimilation (ES-MDA), to incorporate albedo derived from MODIS satellite observations and in-situ mass-balance measurements vis stakes into the full energy balance model CryoGrid applied to Svalbard glaciers. Our primary aim is to enhance the accuracy of both the reconstruction and prediction of glacier mass balance in the Svalbard region through the synergistic use of observational data and model. In a range of experiments, we analyze the performance of different assimilation methods and different observation products. The implementation of ES-MDA has demonstrated marked improvements, while the variations in parameter dynamics have varied effects on the results. We compare the prior and posterior states to help disentangle which process or forcing has the most impact on the uncertainty of the model’s results.

How to cite: Cao, W., Schmidt, L. S., Aalstad, K., Westermann, S., and Schuler, T. V.: Data assimilation in glacier mass balance modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15332, https://doi.org/10.5194/egusphere-egu24-15332, 2024.

Estimations of glacier mass balance are commonly made using field techniques, empirical or physically based models, and remote sensing. More recently, data-driven tools like machine learning have become powerful complements to these conventional techniques. This study explores the potential of using machine learning to simulate the individual point mass balance of 30 sites from 13 glaciers in Switzerland spanning over 60 years, sourced from the Glacier Monitoring Switzerland (GLAMOS) network. To this end, we use two machine-learning models: LASSO regression, a linear regression model with L1-regularisation, and eXtreme Gradient Boosting (XGBoost), a gradient-boosted ensemble of decision trees. The models are driven by temperature and precipitation data at 1 km grid resolution from the Federal Office of Meteorology and Climatology (MeteoSwiss). The seasonal point mass balance data are used to train and test the models for each site individually. A comparative analysis is performed in which the performance of the LASSO regression and XGBoost are compared to a standard approach of calculating mass balance from a temperature-index model. In this analysis, we also explore how different temporal frequencies of climate variables, ranging from annual to monthly, affect the performance of the machine learning methods. Beyond their computational efficiency, these machine learning models are particularly suited to provide valuable insights into feature importance. Harnessing this, we study which months’ temperature and precipitation most significantly contribute to explaining individual stake mass balances and compare these findings with commonly assumed drivers of mass balance.

How to cite: van der Meer, M., Zekollari, H., Huss, M., Bolibar, J., Hauknes Sjursen, K., and Farinotti, D.: Glacier point mass balance modeling using machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2110, https://doi.org/10.5194/egusphere-egu24-2110, 2024.

In our research, we leverage the capabilities of the Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018, respectively, to deepen our understanding of the polar regions. These satellites offer a unique blend of high-resolution Ku-band radar altimetry data, synthetic aperture radar (SAR) mode altimetry, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer. This combination enables the acquisition of both optical imagery and SAR radar altimetry data, extending up to 81 degrees North. Central to our study is the application of deep learning techniques, specifically the Vision Transformers (ViT), which adapt the Transformer algorithm for surface classification in polar environments. This approach is instrumental in distinguishing between sea ice and leads, demonstrating robust performance across various metrics, including accuracy and model roll-out on comprehensive OLCI image datasets. We produce our first lead classification maps at the original OLCI swath level resolution of 300m and a lead fraction prototype mosaic spring pan-Arctic product at gridded level of 1km, 5km and 10km resolution and on daily, weekly and monthly timescales. The use of binned statistics in conjunction with our deep learning classifications provides valuable insights into the spatial distribution and changes of leads within the polar ice. We compare our prototype product with other existing lead products and with auxiliary datasets on thin ice (roughness, thickness). Our work combining different satellite products at pan-Arctic intermediate resolution enhances our capacity to estimate sea ice thickness and aids in forecasting future changes in the Arctic and Antarctic regions, thereby contributing to the field of polar remote sensing with direct applications to the future polar missions CRISTAL and CMIR.

How to cite: Chen, W., Tsamados, M., Willatt, R., Brockley, D., Deisenroth, M., De Rijke-Thomas, C., Francis, A., Hirata, L., Johnson, T., Lawrence, I., Landy, J., Lee, S., Liu, W., Nasrollahi Shirazi, D., Nelson, C., Stroeve, J., and Takao, S.: Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced surface classification in sea ice, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9175, https://doi.org/10.5194/egusphere-egu24-9175, 2024.

Modelling Arctic sea ice dynamics has proven to be a successful application for machine learning, leveraging its ability to generate accurate and computationally efficient forecasts. Nevertheless, prevailing limitations lie in the need for physical interpretability and the inability to unveil the dynamics and interdependencies between relevant ice-related variables and their drivers. In this study, we provide a two-step framework designed to combine the high accuracy and computational efficiency characteristics of machine learning while ensuring high interpretability.

The first step of our framework entails time series clustering to identify subregions that are homogeneous with respect to the spatiotemporal variability in the considered variable and obtain the barycentric time series of each cluster. We then use an advanced feature selection algorithm, the Wrapper for Quasi Equally Informative Subset Selection, that identifies neural predictors, specifically Extreme Learning Machines, to forecast the future evolution of sea ice. It then provides the most relevant set of inputs necessary for accurately describing the evolution for each cluster.

Our investigation focuses on the monthly evolution of sea ice thickness and uses data from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS). Other PIOMAS variables (i.e., sea ice concentration, snow depth, sea surface temperature, and sea surface salinity) as well as observed discharge from five major Arctic rivers (i.e., Ob, Yenisey, and Lena in Asia, Mackenzie and Yukon in North America, provided by the Arctic Great Rivers Observatory discharge dataset) are considered as potential driving factors. 

Our results indicate the pivotal role of past sea ice thickness values, since the forthcoming state of sea ice seems to be influenced by both the current situation and historical trends and periodicity. Sea surface salinity in the open Arctic Ocean is highly persistent, and therefore is not used by the algorithm to explain the sea ice evolution. On the other hand, the Arctic rivers’ flows are more representative of the processes occurring in the clusters along the coast. Finally, the interaction between sea surface temperature and snow depth controls the interplay between ice formation and melting, and therefore plays a significant role in shaping the sea ice evolution in the short term.

Our framework aims to advance our comprehension of the complex physical processes governing sea ice thickness evolution in the Arctic region. Moreover, its effectiveness in uncovering sea ice related processes is expected to further improve with the inclusion of additional input variables and, possibly, of a longer data record.

How to cite: Bianchi, L., Sangiorgio, M., Materia, S., Iovino, D., and Castelletti, A.: Advancing Physical Interpretability of Arctic Sea Ice Dynamics through Automatic Feature Selection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10231, https://doi.org/10.5194/egusphere-egu24-10231, 2024.

The Arctic sea ice has been retreating dramatically in recent years in summer and fall. The navigation season for open water vessels along the Northeast Passage has lengthened to sub-seasonal scales. Accurate perditions of Arctic sea ice in sub-seasonal scales are essential for planning shipping activities. The numerical model cannot achieve a high accuracy of daily sea ice predictions on a sub-seasonal scale. The advanced deep learning brings new solutions for the data-driven-based sea ice prediction.

This study proposed a transformer-based deep learning model to predict multiple sea ice parameters, including sea ice concentration (SIC), sea ice thickness (SIT), and sea ice drift (SID), in the Pan-Arctic in a sub-seasonal scale, 90 days’ lead. An encoder and decoder are constructed based on transformer modules to extract spatio-temporal dependencies from daily SIC, SIT, and SID sequences. The spatio-temporal dependencies at different scales are fused to form the final feature maps. Three SIC, SIT, and SID output modules are designed based on the final feature maps to output different parameters for the next 90 days. The satellite-observed sea ice data from the National Sea Ice Data Center (NSIDC) are employed to train the proposed model. We compared our model with anomaly persistence and the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble predictions to demonstrate the model’s prediction skill.

Further, based on the proposed model, we discuss the effects of typical thermal and dynamic factors on sub-seasonal scale daily sea ice prediction. The selected factors include surface air temperature (SAT), sea surface temperature (SST), surface solar radiation downwards (SSRD), and geopotential height. Finally, we conclude with some scientific guidelines for the sub-seasonal sea ice predictability of the Arctic. 

How to cite: Ren, Y. and Li, X.: Multi-task predictions of the Arctic sea ice by a transformer-based deep learning model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13556, https://doi.org/10.5194/egusphere-egu24-13556, 2024.

Melt ponds are pools of water that form during summer on the surface of the arctic ice. Due to the lower albedo, melt ponds absorb more solar radiation than surrounding ice and hence have higher temperature. This causes more water to melt, creating a feedback loop. This means that melt pond fraction in ice sheets is an important factor to consider in global climate and sea ice models. In situ measurements are difficult and expensive in terms of time and labor. Furthermore, these measurements can only cover limited areas. This makes using Earth Observation methods for this task particularly attractive.

Until today, there is no sophisticated global melt pond data set available:

Accurate methods may exist for determining melt ponds from Sentinel-2 data. The downside of using Sentinel-2 is that parts of the High Arctic are not covered by this mission.

MODIS data covers the whole globe at least once every three days, but the downside of it is that MODIS resolution is much coarser (250m  vs. 10m). Since melt ponds are in general much smaller than 250m, it means that accurately capturing melt pond fraction from these data is difficult.

We propose to address these issues by employing Deep Learning techniques. Namely, we use Sentinel-2 data to train a model to super-resolve MODIS images to higher resolution and to use all available MODIS bands and their surrounding pixels for information context when predicting melt pond and open water fractions.

In addition, a thorough uncertainty quantification (UQ) will be applied by using the UQ Toolbox.

How to cite: Rösel, A., Neckel, N., and Jancauscas, V.: Arctic Meltponds: Automated Detection Algorithm Using Enhanced Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16672, https://doi.org/10.5194/egusphere-egu24-16672, 2024.

Monitoring the dynamics of Antarctic supraglacial lakes is of particular interest in the context of global warming. Supraglacial meltwater accumulation on ice sheets and ice shelves can be a major driver of accelerated ice discharge. This is caused through processes such as surface runoff leading to ice thinning, basal meltwater injection causing basal sliding, and hydrofracture triggering ice shelf collapse and subsequent glacier acceleration. In addition, an increased presence of supraglacial lakes around the Antarctic margin can trigger enhanced melting due to the low albedo of surface lakes, which leads to increased absorption of solar radiation. Hence, continuous monitoring of supraglacial lakes is crucial for improving our understanding of their seasonal variations in extent and their impacts on ice shelf stability and ice sheet surface mass balance. Initially, an automated supraglacial lake mapping approach was developed to create bi-weekly lake extent maps for six Antarctic ice shelves based on fused results from convolutional neural network predictions and a Random Forest (RF) classification trained on Sentinel-1/-2 data. However, regular large-scale monitoring beyond these six selected areas requires a model with higher spatio-temporal transferability and an efficient fully automated data processing workflow. We tested for a potential improvement by replacing the RF-based mapping with an attention-based U-Net, expanding the training and test sites on a total of 23 regions and switching the processing to a more powerful high-performance computing infrastructure. We will discuss how remote sensing-based mapping accuracies can be improved by extending the training/test dataset, selecting the right machine learning model and the choice of processing infrastructure. In the future, the automated processing workflow will provide a regularly updated dataset on supraglacial lake dynamics of 23 Antarctic ice shelves via a web service by exploiting the full archive of available Sentinel-1/-2 satellite imagery.

How to cite: Baumhoer, C. A., Koehler, J., Lhermitte, S., Wouters, B., and Dietz, A. J.: Towards monitoring supraglacial lake dynamics in Antarctica with convolutional neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14894, https://doi.org/10.5194/egusphere-egu24-14894, 2024.