Multi-mission satellite altimetry data have been used to study the spatial and temporal variability in global storm surge water levels. This was done by means of a time-dependent extreme value analysis applied to the monthly maximum detided water levels. To account for the limited temporal resolution of the satellite data, the data were first stacked on a 5^∘× 5^∘ grid. Moreover, additional scaling was applied to the extreme value analysis for which the scaling factors were determined by means of a resampling method using reanalysis data. In addition to the conventional analysis using data from tide gauges, this study provides an insight in the ocean-wide storm surge properties. Nonetheless, where possible, results were compared to similar information derived from tide gauge data. Except for secular changes, the satellite-derived results are comparable to the information derived from tide gauges (correlation > 0.5), although the tide gauges show more local variability. Where limited correlation was observed for the secular change, it was suggested that the satellites may not be able to fully capture the temporal variability in the short-lived, tropical storms, as opposed to extra-tropical storms.

In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, including a thresholding method, nine supervised-, and two unsupervised machine learning methods, applied to Sentinel-3 Synthetic Aperture Radar Altimeter data. For the first time, the simultaneously sensed images from the Ocean and Land Color Instrument, onboard Sentinel-3, were used for training and validation of the classifiers. This product allows to identify leads that are at least 300 meters wide. Applied to data from winter months, the supervised Adaptive Boosting, Artificial Neural Network, Naïve-Bayes, and Linear Discriminant classifiers showed robust results with overall accuracies of up to 92%. The unsupervised Kmedoids classifier produced excellent results with accuracies up to 92.74% and is an attractive classifier when ground truth data is limited. All classifiers perform poorly on summer data, rendering surface classifications that are solely based on altimetry data from summer months unsuitable. Finally, the Adaptive Boosting, Artificial Neural Network, and Bootstrap Aggregation classifiers obtain the highest accuracies when the altimetry observations include measurements from the open ocean.

Previous studies have demonstrated that tides are subject to considerable changes on secular time scales. However, these studies rely on sea level observations from tide gauges that are predominantly located in coastal and shelf regions and therefore, the large-scale patterns remain uncertain. Now, for the first time, satellite radar altimetry (TOPEX/Poseidon & Jason series) has been used to study worldwide linear trends in tidal harmonic constants of four major tides (M₂, S₂, O₁, and K₁). This study demonstrates both the potential and challenges of using satellite data for the quantification of such long-term changes. Two alternative methods were implemented. In the first method, tidal harmonic constants were estimated for consecutive 4-year periods, from which the linear change was then estimated. In the second method, the estimation of linear trends in the tidal constants of the four tides was integrated in the harmonic analysis. First, both methods were assessed by application to tide gauge data that were sub-sampled to the sampling scheme of the satellites. Thereafter the methods were applied to the real satellite data. Results show both statistically significant decreases and increases in amplitude up to 1 mm/year and significant phase changes up to ∼0.1 deg/year. The level of agreement between altimeter-derived trends and estimates from tide gauge data differs per region and per tide.

Seasonal modulation of the M₂ tide has been quantified for the entire Arctic Ocean and connected regional seas, using tidal harmonic analysis of water levels derived from Synthetic Aperture Radar altimetry. Results are compared to numerical simulations that model the effect of two limiting cases of seasonal landfast ice cover on the M₂ tide. The largest seasonal modulation (up to 0.25 m) is observed along coastlines and in bays. Locally, the presence of landfast ice decreases amplitudes, but in some cases, the opposite effect was observed further afield. In most of the Arctic, winter months experience a later arrival of the tide, except for Hudson Bay where phase advance is observed. Most of the altimeter-derived seasonal modulation could be explained by the modeled impact of landfast ice. However, particularly in the Hudson Bay system there is a discrepancy between model- and altimeter-derived seasonal modulation. This suggests that other seasonal processes are important. Finally, results suggest that the consequences of variations in Arctic landfast ice are not restricted to the Arctic, but affect tidal water levels on a global scale.

Tidal marshes play an important role in climate change mitigation through natural coastal protection. The effectiveness of the natural coastal defense by tidal marshes is closely related to their channel network which is in turn greatly influenced by their vegetation cover and shape. Previous research suggests a dual effect of vegetation on marsh topography; stabilizing sediment on the one hand versus promoting erosion and channel incision on the other hand. This study links these effects to different vegetation species, Salicornia procumbens, Spartina anglica, and Puccinellia maritima (further referred to as Salicornia, Spartina, and Puccinellia), by means of a coupled bio-hydromorphodynamic modeling study. Single species, species-assemblages, and species shifts were studied, incorporating both species-specific physical plant properties and spatiotemporal growth strategies. The results indicate the influence of vegetation on the marsh topography to be highly species-dependent, but also of a very complex nature. Both the presence of Spartina and Puccinellia resulted in significant channel development, whereas Salicornia did not induce topographic change. The combination of several species promoted or reduced channel development depending on the included species. Species-shifts linked with climatic changes resulted in increased erosion of the existing channel network potentially reducing the protective capacity of the marsh.