Global warming is accelerating the melting of glaciers and ice sheets, leading to the formation of supraglacial meltwater, which accumulates in supraglacial lakes and significantly impacts glacier stability. Supraglacial channels can redistribute meltwater and improve glacier stability and are currently monitored using optical data from Sentinel-2. Using Sentinel-1 synthetic aperture radar data omits issues related to cloud cover and darkness, which is the primary focus of this study. The study focuses on three glacier site subsections: Nioghalvfjerdsbrae Glacier, Humboldt Glacier, and Russell Glacier. Sentinel-1 data is compared to the well-established optical data from Sentinel-2, which serves as the reference for channel delineation. The methodology encompasses both thresholding techniques and a GLCM-assisted random forest regression for co- and cross-polarized Sentinel-1 data, while Sentinel-2 data utilizes a thresholding technique based on Glen et al. (2024) and a path-opening algorithm developed by Yang et al. (2015) to delineate narrow channels from enhanced NDWI images. Google Earth Engine is employed for data access and preprocessing, whereas Python is leveraged to conduct the subsequent analysis. Preliminary studies have shown that individual Sentinel-1 scenes may only partially capture supraglacial channels. Therefore, individual scenes are aggregated by taking the backscatter minimum (assumed to be indicative of water due to specular reflection) and standard deviation (representative of change) over a melt season. Composite images offer a more comprehensive view of the supraglacial drainage network during the melt season and form the basis for GLCM texture feature calculations. A 3-by-3 kernel is used around pixels to generate an additional 64 input features by calculating the contrast, correlation, heterogeneity, and dissimilarity over 4 discrete angles for the HH and HV minima and variance. The backscatter features are fed through a random forest regressor with the per-pixel water fraction (i.e. the fraction of which each pixel is classified as water over all images) as the target variable derived from the Sentinel-2 reference classification, providing a more flexible regressor than a binary distinction. Results indicate that channels with sufficient cross-sectional area are detectable in Sentinel-1 data for both polarizations, albeit with varying success. Challenges such as speckle noise, channel size, the heterogeneity of pixel elements, and the movement of glaciers complicate the analysis. The study finds that while thresholding outperforms GLCM-assisted regression, both methods struggle with speckle noise inherent in Sentinel-1 data and alignment issues between the model input and reference classification. Despite these challenges, GLCM-assisted regression shows promising results by leveraging textural information and outweighing backscatter minima and variances in terms of feature importance. To conclude, Sentinel-1 data can be incorporated to delineate channels of sufficient size. Channels approaching the pixel resolution are captured partially at best and require image compositing. The variability in terrain and channel features, alongside persistent noise, limits the accuracy and reliability of this approach. GLCM texture feature analysis and random forest regression show promising results despite the multitude of problems. Other machine-learning techniques should be explored prior to continuing with this approach.

Globally, coastal communities face increasing risks from climate-related hazards such as flooding, shoreline erosion, and salt intrusion. These hazards pose threats to both people and their environment, with extreme sea level events increasing these risks. Satellite altimetry allows for global observation of the sea level, reaching remote regions that are not covered by unevenly distributed tide gauges, as these are concentrated in densely populated regions of Western cultures. However, their 10- to 35-day repeat cycles complicate the capture of extreme sea level events. Machine learning offers a promising approach to combine direct satellite altimetry observations with ERA5 pressure and wind speed fields into a data-driven model. As opposed to global and regional numerical models, which require substantial time and expertise to develop, machine learning models are time efficient and require relatively low effort to develop and expand.

This study presents a shallow neural network that effectively estimates hourly non-tidal water levels in the Dutch coastal zone, using X-TRACK retracked and reprocessed satellite altimetry observations and ERA5 hourly pressure and wind speed fields. Reprocessed satellite altimetry observations from 11 missions are used to provide more accurate coastal observations. Tide gauge records are used as ground truth. Both tide gauge and satellite altimetry data are corrected for harmonic tidal signals before training. A 48-hour time window is applied, using all data from 48 hours to 1 hour prior to the estimates as input into the network. The area of interest covers most of the North Sea, from the Strait of Dover to the northern North Sea, excluding the Danish and Norwegian coasts. The neural network is trained and tested at three locations: Scheveningen, Vlissingen, and the Europlatform.

Results show that the neural network can estimate hourly non-tidal water levels with mean squared errors ranging from 0.011 to 0.018 m, mean absolute errors from 0.078 to 0.101 m and standard errors from 0.100 to 0.134 m. K-fold cross-validation with K=4 indicates high robustness, with mean squared errors varying by 0.004 m, mean absolute errors by 0.012 m and standard errors by 0.017 m. The model performs best for hourly and high water levels at the Europlatform and worst for high water levels at Scheveningen. This is partly due to the location of the Scheveningen tide gauge in a harbour with more localised disruptions of the water level compared to the tide gauge at the Europlatform. The ERA5 longitudinal wind speed component contributes most to the estimation of non-tidal water levels, accounting for $\pm$18\% of all weights corresponding to the input variables. Key regions for the estimation of non-tidal water levels include the Dutch coast and the northern North Sea.

When compared to a local numerical model, the developed neural network does not perform with the same accuracy. However, several upsides of the model are identified, such as high computational efficiency for single locations and easy implementation options for refinement of the model. Recommendations for future research focus mostly on improving the model's performance on high water levels and applicability to different regions.

The following report investigates the land ice height decrease of the Fleming Glacier between 2019 and 2022 using ICESat-2 satellite data. This glacier is located on the Antarctic Peninsula, an area that has been severely impacted by global warming. Using data from the Advanced Topographic Laser Altimeter System (ATLAS) on board ICESat-2, more specifically its ATL06 product, the average land ice heights in 2019 and 2022 could be compared. This was done with the aid of Python and the icepyx library, which allows for an easy extraction of the desired data from the NASA Eathdata website. The raw data had to be processed and filtered to eliminate the NaN values and reduce the noise. The resulting height measurements were then plotted and an average rate of land ice height decrease of 4.40 metres over the 3-year period was found, which corresponds to a 1.47 m/year decrease. The findings of this study indicate a slightly lower value compared to the results reported by Friedl et al. in their 1994-2016 study. However, this discrepancy is plausible, particularly considering the episode of increased ice melting observed in the Fleming Glacier after 2008, which can be attributed to the disintegration of the Wordie Ice Shelf. Crucially though, due to an issue with the Reference Ground Track overlap the amount of common data points found was just 27. This is insufficient to draw definitive conclusions regarding the overall melting of the entire Fleming Glacier. Future research, especially involving the use of the ATL11 product, is therefore recommended for this region.

Atmospheric rivers transport 90% of all atmospheric moisture in the mid-to-high latitudes, while covering only 10% of the Earth’s surface at any given time. Atmospheric rivers occur infrequently, and atmospheric river frequency in the polar areas is especially low, but they can have a large impact on the cryosphere when they make landfall. They have been linked to various processes affecting the surface mass balance, such as extreme precipitation events as well as surface melt.
This thesis addresses the importance of atmospheric rivers in the polar regions. The study aims to compare atmospheric river precipitation estimates obtained from reanalysis data with ICESat-2 satellite altimetry observations in Antarctica between 2019 and 2021, to determine whether the two different types of data sets show similar amounts of atmospheric river precipitation.
To achieve this goal, an atmospheric river detection algorithm designed specifically for polar regions was used to identify atmospheric rivers in Antarctica. All precipitation falling within an atmospheric river footprint for the first 24 hours after detection is attributed to the atmospheric river. The detection algorithm and precipitation attribution are performed to MERRA-2 and ERA5 reanalysis data. The resulting atmospheric river precipitation anomalies were then compared to ICESat-2 height change observations using correlation analysis and a metric based on variance reduction. A detailed analysis
is presented of specific drainage basins that show promising results based on the comparison of the reanalysis data and ICESat-2 observations, using time series.
The results show a high degree of correlation between the atmospheric river precipitation anomalies from reanalysis data and ICESat-2 height changes in multiple drainage basins. Variance reduction shows that atmospheric river precipitation can explain a significant part of the variance of the ICESat2 height change observations in these drainage basins. This suggests that in select locations, the atmospheric river precipitation expected based on reanalysis data is indeed observable in ICESat-2 data. A challenge is the coarse temporal resolution of ICESat-2 data. ICESat-2 data has a temporal resolution of 91 days, whereas atmospheric rivers typically last between a few hours up to a few days
at most, and occur very infrequently (up to ∼2% of the total time over the time period 2019-2021).
Nonetheless, this thesis provides a comprehensive analysis of atmospheric river precipitation in Antarctica using both reanalysis data and satellite observations, contributing to a better understanding of the impact of atmospheric rivers on the surface mass balance of Antarctica. Additionally, it suggests that as long as its limitations are taken into account, ICESat-2 data can be a valuable tool to use in addition to reanalysis data in the study of atmospheric rivers in the polar regions.

In September 2022, Greenland experienced an extraordinary late-season melt event, characterized by temperatures exceeding the melting point at Summit Station for the first time on record and surface melt appearing across one-third of the ice-sheet. This thesis investigates extreme melt events at the Summit in Greenland, focusing on the attribution of the September 2022 extreme melt event to human-induced climate change. The study combines observational data and climate model simulations to assess the influence of climate change on these events and project their likelihood in the future. The research involved identifying melt events in observational and model data. Subsequently, melt-event probability ratios were calculated between the pre-industrial, current, and future climates. These ratios were synthesized to form an attribution statement and provide insights into future scenarios. The study reveals that melt events in any month at the Summit in Greenland have become 20 times more likely in the current climate compared to the pre-industrial climate. This increase in likelihood of melt events in any month is significant and can be attributed to human-induced climate change. However, for melt events specifically in September, although unprecedented in pre-industrial and recent times, no significant increase is found due to a lack of data. Definitive conclusions are expected with more data. Projections based on climate models indicate a substantial rise in future melt event probabilities, reaching up to a 46% chance of Summit melt in September and a 83% chance throughout the remainder of the year. The findings suggest that, while the September 2022 event cannot definitively be attributed to climate change, it highlights the increasing likelihood of such events and their potential impact on sea levels. However, the analysis carries inherent uncertainties due to limited historical and climate model data usage and limited consideration of atmospheric river circumstances. Despite these challenges, these insights contribute to enhancing our understanding of extreme melt events and, in turn, inform the formulation of future climate mitigation and adaptation strategies.

Disintegration of Antarctic ice shelves can induce devastated consequences for the environment and human infrastructure in the form of an increase of the global mean sea level. One of the causes of an ice shelf break down is hydrofracturing due to the mass load of supraglacial lakes. The top of the snowpack melts and the meltwater flows to a local depression where it accumulates and forms melting ponds. Detecting and quantifying the depth of these supraglacial lakes will increase the knowledge on the evolution of supraglacial lakes. Satellite remote sensing techniques are able to determine the volume of individual ponds. However, these methods have their limitations in calculating the depth and area per lake. Regional climate models are capable of estimating the total volume of meltwater within a certain area of interest, but have until now not been able to measure the depth and area of separate supraglacial lakes with certainty. This research study focused on highlighting the limitations of and developing possible improvements for three climate-based and satellite-based methods for comparing them to one another. The first method made use of Sentinel-2 scenes and a threshold-based classification to calculate the water extent and the water depth was calculated by use of band values and by knowing both the volume can be estimated. The second method made use of Sentinel-1 and Sentinel-2 images to classify areas containing water over a biweekly interval. A new method, denoted as the kernel method, was developed for measuring the depth of each detected lake with a lake mask and a digital elevation model. The volume is subsequently derived from the water extent and depth. Snowmelt, refreezing, precipitation and snowfall from a regional climate model, RACMO, was applied to estimate the total volume of meltwater within a catchment. With a digital elevation model a routing is determined to visualize where the calculated meltwater accumulates and subsequently the depth and area were computed. Based on comparing the water extents over the period of 2016-2021 on the Nivlisen Ice Shelf the following can be concluded: the climate model-based method cannot produce realistic water extents (the results were ten times larger than the satellite-based methods); the different classification methods have similar outcomes, the thresholds of the method using solely Sentinel-2 are preferred; The satellite methods are limited by clouds and frozen ice lids. The results of the water extents indicated unnatural large depths (average +30 $m$) and that the satellite remote sensing methods produce water extent in the same order of magnitude. In addition, the kernel method showed potential, since it can be executed without non-optical satellite data. However, in order for it to improve, the size of the kernel needs to be optimised. The total volumes are in the same magnitude range, but the climate data method overestimates ( getal) due to the fact that the maximum value is chosen within the biweeks. In addition, this study's resulting volumes are close to the values computed by Van der Zalm (2020) and Dell et al (2020), which increases the confidence in the results. However, a problem that arose is the absence of a ground truth to accurately compare the results with and therefore it is recommended to possibly compare the data to that of an altimeter. Additional improvements can be made in kernel size optimization based on the middle line of the lakes and developing a method to correctly locate and calculate the depth of water using a total runoff and a digital elevation model.

Antarctic ocean temperatures are rising due to climate change, causing land ice to melt at increasingly higher rates. Ice shelf bottom melt is a key factor responsible for Antarctic ice mass loss and as such understanding melt processes in the Antarctic is therefore key to more accurately predict how the global sea level will respond to climate change in the foreseeable future. Basal melt results in the formation of both basal melt channels underneath an ice shelf and persistent sea ice wakes (named plume-driven polynyas) at the ice shelf shoreline. The goal of this research is to develop a method that can help to automatically infer basal melt locations along the Antarctic shoreline with significantly increased spatio-temporal resolution compared to previously researched basal melt detection methods.
We infer basal melt locations by detecting plume-driven polynyas. We used dual-pol (HH/HV) Sentinel-1 EW SAR data (40x40m resolution) in combination with GLCM textural features as input for a random forest classification that differentiates images as water or ice in four sub-classes: undisturbed ’open’ water, disturbed ’rough’ water, sea ice and (floating) land ice. We assessed what the advantages and limitations of this approach were for plume-driven polynya detection by performing water-ice (sub-class) classifications and examining which GLCM features proved most useful, what GLCM window size is preferred, and how classification can be aided by post-processing classified images.
We computed GLCM textures for window sizes w = [5,11,21] and created a classifier for each choice (GLCM5, GLCM11 and GLC21) and compared results to a classifier based on original dual-pol SAR data (BASE). Via cross validated recursive feature elimination we determined that ’sum average’ (HH and HV polarization) and ’difference variance’ (HV polarization) were most useful for separation of water and ice classes (HH_savg, HV_savg and HV_dvar). Our results have shown that using GLCM texture based dual-pol classifiers improves water-ice classification significantly compared to dual-pol only classifiers, although using HH_savg and HV_savg instead of orignal dual-pol data comes at a cost of reduced spatial resolution. Water-ice classification accuracy of BASE was 92.2% (kappa = 84.4%) was increased to 95.9% (kappa = 91.5%) for GLCM5, 96.3% (kappa = 92.7%) for GLCM11 and to 96.5% (kappa = 93.0%) for GLCM21. From a spatial context, GLCM21 showed an insufficient ability to detect small-scaled bodies of water at a sub-kilometer scale. GLCM5 showed unsatisfactory results in terms of sea ice classification. GLCM11 showed highest robustness in both these performance aspects and proved to be most successful classifier for the application of polynya detection. Using an area filter as a post-processing step proved successful when a classifier is based on GLCM data with a window size no larger than w=11. Noise output (small regions of falsely classified open water pixels) was heavily reduced via this form of post-processing and significantly increased polynya detection performance.
The final classified product however still contained too many incorrectly classified water regions of similar spatial scales as plume-driven polynyas to be able to apply this algorithm as a reliable automated polynya detection method. We urge to build upon this SAR-based detection method, by using additional non-GLCM input features or using extra post-processing steps, such as temporally filtering water body presence, until results are satisfactory for a fully automated plume-driven polynya detection algorithm. The method presented here has the potential to make detection significantly faster, easier and more accessible than the current methods available. Lastly, in its current state, this method can already be used to validate predicted locations of basal melt by ocean-ice sheet models and DEM-based methods.

The Jakobshavn glacier was responsible for approximately 1 mm eustatic sea level rise in the period of 2000 to 2010 [Howat et al. 2011]. As such, the Jakobshavn glacier became one of the largest outlet glaciers in Greenland [Joughin et al. 2004]. Ice flow velocities within the same period reached over 10 km/yr with strong seasonal variation [Howat et al. 2011, Joughin et al. 2012]. More recently from 2011 until 2013, even higher ice flow velocities of at least 15 km/yr were observed [Lemos et al. 2018]. Due to the relatively high ice flow velocities, the ice discharge plays the largest role in the mass balance (MB) of the Jakobshavn glacier. Quantification of the ice discharge from ice flow velocities is however, not a common procedure. Yet the evolution of the ice discharge of single glaciers not only improves understanding of the climate-cryosphere system, but also aids quantification of sea level contribution on a drainage basin scale. To that end, this study embodies an indirect ice discharge estimation of the Jakobshavn glacier over the period of November 2010 until March 2016 using altimetry (CryoSat-2 Level 1b (L1b)) and gravimetry (GRACE Level 2 (L2)) data in combination with a regional climate model (RACMO 2.3p2). By subtraction of the altimetric and gravimetric mass balance estimates from the atmospheric component (i.e. the surface mass balance (SMB)), two ice discharge estimates are obtained. This approach does not suffer from the drawbacks involved when estimating ice discharge from velocity fields directly, which are based on offset tracking. Data gaps for long polar nights and clouds in the visible spectrum and decorrelation in general, when the duration between subsequent images over the same location is long, are thus avoided. This is because offset tracking algorithms require recognisable characteristics in subsequent satellite recordings to determine the velocity, i.e. satellite recordings need to be sufficiently correlated. To derive the mass balance from altimetry data, adequate spatial sampling is desired. To that end, this study applies swath processing to CryoSat-2 L1b data with an adapted waveform sample selection criterion to obtain an unprecedented spatial sampling with about 2 order of magnitude more height observations compared to conventional retracking techniques. As a consequence, elevation changes can be derived at a relatively high spatial resolution (250 m).
The elevations are converted to elevation changes, volume change and mass change using weighted least squares estimations (WLSE), hypsometric averaging and density models, respectively. The GRACE-based mass change estimate is acquired using a point-mass assumption at the location of the Jakobshavn glacier. The known, simulated point mass is then scaled to the observed mass by GRACE. In addition, data weighting of GRACE Stokes' coefficients is attempted using the full noise covariance matrix. Subsequently, a LSE is used to infer the mass balance from the two time series (with and without weighting of the Stokes' coefficients).