The Karnali River in Bardia National Park (BNP), Nepal, is crucial for sustaining the habitats of endangered Bengal Tigers. However, gathering data to analyze this remote river’s behavior presents significant challenges due to the remote and mountainous terrain of BNP.

This thesis explores an approach using the altimetry satellite Sentinel-6 to measure river water surface elevations (WSEs), specifically utilizing the innovative Polygon-Informed Cross-Track Altimetry (PICTA) method. By integrating fully-focused Synthetic Aperture
Radar (FF-SAR) data from Sentinel-6 with a static river polygon, we aimed to retrack WSEs and unlock new insights into the Karnali River’s dynamics.

The objective of this thesis was to evaluate the performance and potential of the PICTA method in deriving precise river level profiles for the Karnali River. The research questions addressed include: How does PICTA compare to in-situ water surface height measurements? How well does it align with the SWOT mission, which also measures river water levels? How do river level profiles change over time? And how does the use of a static river polygon influence water level uncertainty for the dynamic Karnali River?

The PICTA method successfully derived water surface elevations for a 10-kilometer-long section of the Karnali River. Over a year-long period from February 2023 to February 2024, we generated 38 detailed PICTA river level profiles. These profiles revealed the relationship between measured WSE fluctuations, river slope, and river width over time. We observed that both WSE fluctuations and river slope decreased at sections where the Karnali River could overflow its banks during high water. Notable gaps in the profiles, such as transitions from
a single channel to a multichannel system, provided insights into the interaction between river characteristics and the PICTA algorithm.
The PICTA-derived WSE time series closely matched in-situ measurements at the river gauge station at Chisapani, Nepal, showing similar seasonal trends and peak differences. When comparing PICTA and SWOT profiles, we observed a mean bias near zero and a scaled MAD of approximately 20 cm. Dynamic river polygons based on SWOT data further improved the agreement, reducing the scaled MAD by 10 cm and increasing retracked PICTA data points. Using a static river polygon introduced WSE uncertainties ranging from 5 cm in the summer to 20 cm in the winter, averaging 10 cm over time. This study suggests that dynamic polygons could enhance the accuracy of PICTA derived WSEs. Ultimately, PICTA’s ability to capture and relate seasonal trends to local hydraulic behavior underscores its significant potential. This research advances our understanding of the Karnali River’s dynamics and demonstrates
PICTA’s ability to derive river water levels in a remote, mountainous region. These insights could constribute to support better conservation efforts for BNP’s vital Bengal Tiger habitats.

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.

Rivers play a crucial role in shaping landscapes and supporting ecosystems. This is demonstrated by the tiger habitats in and around Bardia National Park in West-Nepal, which rely on the Karnali River. This study contributes to a larger effort aimed at sustainably managing these tiger habitats. Monitoring the rivers in this remote area is challenging, suggesting a role for remote sensing. An exploration is presented regarding the potential of satellite synthetic aperture radar altimetry (sat-SARA) for monitoring rivers situated in diverse topographic landscapes. Focusing on the Bheri, Karnali, and Geruwa Rivers, the applicability of sat-SARA techniques for water level monitoring, multiple channel identification, and channel activation detection was evaluated. For deriving water surface heights from sat-SARA data, an empirical Gaussian retracker was used. The findings are promising. While resulting water level variations align with field observations, complementary in-situ measurements are imperative for a comprehensive evaluation. Additionally, the study reveals the potential for identifying multiple channels from sat-SARA return signals, extending to channel classification and detecting channel activation. Leveraging the labour-intensive nature of sat-SARA data processing, the technique holds great promise for monitoring rivers in remote and difficult-to-access landscapes. Therewith, this study contributes to advancing the understanding of the hydrodynamics of the Lower Karnali River and opens doors for sat-SARA applications for river monitoring in challenging terrains.

Subsurface firn processes play a crucial role in ice sheet mass loss mechanisms. On Greenland surface meltwater percolates to deeper layers where porous firn retains it, directly inhibiting runoff. However, secondary effects such as the formation of impermeable ice slabs may indirectly and irreversibly accelerate runoff and with it global sea level rise. Microwave remote sensing offers opportunities to monitor these processes, but due to the simplicity of their underlying snow models retrieval methods fail over areas subject to melt and refreezing - areas where the firn's (in)ability to buffer meltwater is critical. This study presents a new forward model which given initial conditions and atmospheric forcing first solves for the firn state through a full-complexity snow model (SNOWPACK) and then simulates multifrequency brightness temperature (Tb) time series (using radiative transfer model SMRT). As part of a comprehensive sensitivity analysis three ensembles of multi-decade Tb time series (19 and 37 GHz) were modelled for the DYE-2 site in the percolation area of the Greenland Ice Sheet. Model performance based on RMSE w.r.t. independent Tb satellite observations was found to be sensitive to biases introduced in the atmospheric forcing record (with air temperature, precipitation and relative humidity controlling variance) and snow model settings (new snow grain size and albedo settings) and not to initial profile conditions. However, computed RMSEs were high (min. 17.8 K at 37V and 19.4 K at 19V) due to trends in modelled Tb consistently underestimating observed trends when taken over an accumulation season. It is shown that this can only be explained by the constant-with-time stickiness assumption used to link the snow model’s microstructure representation to the sticky hard sphere model employed for the radiative transfer scheme. A seasonal stickiness signal is made evident for the conditions at DYE-2 and linked to its yearly melt-refreeze-accumulation cycle. These results demonstrate that earlier approaches to forward model microwave satellite observations based on a constant-with-time stickiness assumption (or that lack a third snow microstructure parameter altogether) are not valid for ice sheet areas prone to melt. This study is expected to be the starting point for a more sophisticated implementation that estimates a snow layer's stickiness from microstructure information already available in the snow model. If successful it would be the first of its kind and open the door to satellite-based retrieval of subsurface firn properties and processes from areas where observations are currently lacking, greatly reducing uncertainty in ice sheet mass loss and global sea level rise projections.

The purpose of this thesis is to do quality assessment of GNSS/IMU derived NAP heights for The Netherlands (NAP) using Fugro RILA technique and the RDNAPTRANS2018 published by Rijkswaterstaat. The use of Global Navigation Satellite System (GNSS) is growing rapidly in order to determine the position (both horizontal and vertical). However, the GNSS is only able to give the geometric height which is the position on the ellipsoid, which have a drawback that the surface of constant ellipsoidal height are not equipotential surface, and hence these heights need to be transformed into traditional height systems such as the Normaal Amsterdam Peil (NAP) used in The Netherlands. In order to derive these NAP heights from the GNSS heights, a (quasi-) geoid model along with corrector surface model is used to convert from one height to another. In this study the newly computed local quasi geoid model NLGEO2018 rather than the NLGEO2008 is adapted using RDNAPTRANS2018 along with Fugro Rail Infrastructure aLignment Acquisition (RILA) technique which makes use of GNSS/IMU to obtain ellipsoidal height. It was found upon using the older transformation procedure RDNAPTRANS2008, which makes use of NLGEO2008, a mismatch in the order of 17mm between the NAP and GNSS-derived NAP from RILA. This error is not only due to the geoid itself but also the systematic errors in the different height system. However, this new geoid NLGEO2018 along with the new transformation procedure of RDNAPTRANS2018 allows a more accurate conversion of ellipsoidal to normal heights and this study focuses on this quality assessment of the new GNSS/IMU derived NAP heights and investigate how well NAP heights be obtained using RILA technique based on the new geoid. It was found with the use of the new geoid and the new transformation model RDNAPTRANS2018, the mean height difference was reduced from 12mm to 3.6mm showing a better fit of GNSS heights to NAP heights. An error budget was also calculated, to understand the reliability of these RILA measurements with the NAP heights. This error budget includes all the uncertainties from all error sources, namely RILA derived height, geoid height and the levelled height, followed by hypothesis testing. The highest uncertainty was from GNSS/IMU measurements from RILA system followed by the levelling and then the gravimetric quasi-geoid. Finally from this analysis, areas where the terrestrial surveying can be avoided was found. It was found that near the stations, tunnels, high vegetation had the highest uncertainty due to poor GNSS reception. It was also found that level crossing also showed a constant systematic offset between the two heights potentially due to the materials of the level crossings.

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).

As extensive efforts from consumer drone manufacturers resulted in inexpensive aircrafts that can capture high quality video imagery, drones are increasingly considered to be beneficial for scientific purposes. In the recent past, video imagery has been used to analyze waves in terms of several hydrodynamic parameters and to indicate matching coastal features. Whereas these measurements have been acquired using static cameras mounted on large poles situated at beaches, this report exploits a recently developed method using Unmanned Aerial Vehicles (UAVs) as a means to map coastal morphology. After recording aerial imagery in combination with several Ground Control Points (GCPs), several time series of georectified coastal images are compiled. Subsequently, for all of the points in a predefined grid, pixel intensities are stored throughout the time of the recording. Consequently, hydrodynamic data like wave celerity and phase are estimated, which in turn are used to invert water depths for every location in a predetermined area of interest using an algorithm called cBathy. Using reference measurements with an accuracy in the order of five centimeters, this report benchmarks the bathymetry as computed using the drone imagery by calculating the root mean squared error, the root mean squared error divided by the water depth and the mean error of three different sub areas within the area of interest. After calibrating parameters as used by the cBathy algorithm, it is shown that the best computation yielded a bathymetry with a root mean squared error of 0.37 meters for a total area of approximately 2500 square meters. It is also shown that the other datasets yield errors of approximately twice the error of the best dataset. It is shown that the total error can to a large extent be attributed to errors in the rectification part of the algorithm. The large errors and the large discrepancy between the errors make the method currently unsuitable for coastal monitoring purposes. Hence, before the UAV bathymetry mapping method is to replace traditional methods, more research should focus on standardizing the process and thereby decrease the variance between the errors of different datasets.