The increasing frequency of extreme weather events intensifies the demand for accurate monitoring of nearshore wave dynamics, as these dynamics directly affect shoreline stability, flood risks, and coastal operations. While operational wave models provide essential forecasts, the
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The increasing frequency of extreme weather events intensifies the demand for accurate monitoring of nearshore wave dynamics, as these dynamics directly affect shoreline stability, flood risks, and coastal operations. While operational wave models provide essential forecasts, they often underestimate significant wave height (SWH) during energetic conditions and omit infragravity (IG) waves. The latter can substantially amplify coastal impacts such as flooding, dune erosion, and harbor seiching. These limitations are further exacerbated by the limited spatial coverage of in-situ buoys, making model validation in coastal waters particularly challenging.
The launch of the Surface Water and Ocean Topography (SWOT) mission presents a promising yet underutilized opportunity to improve coastal monitoring and support the validation of hydrodynamic models. Using Ka-band Radar Interferometry (KaRIn), SWOT provides high-resolution, two-dimensional measurements of sea surface height (SSH), offering spatial detail that exceeds the capabilities of buoys and traditional altimeters. While initial validations in the open ocean have confirmed SWOT’s capacity to retrieve SWH and resolve long-period waves, its performance in shallow, morphologically complex coastal seas remains largely untested.
This research presents a novel approach comprising (i) a multi-pixel match-up strategy for SWH retrieval, and (ii) the first satellite-based spectral analysis for detecting IG wave energy in the Southern North Sea. Through three targeted case studies, the study investigates SWOT’s two-dimensional SSH and SWH patterns, retrieval accuracy, and sensitivity to key processing parameters across varying sea states and bathymetric regimes. Validation is performed using in-situ buoy observations, platform-mounted radar measurements and numerical wave models to assess consistency, spatial performance, and retrieval accuracy.
By applying both single- and multi-pixel match-up strategies, our findings revealed the trade-off between statistical variance reduction through spatial averaging and the preservation of local wave variability. Similarly, for spectrally derived IG wave energy, different analysis box sizes were evaluated against platform-mounted radar observations in the Southern North Sea. Results show that SWOT-derived SWH exhibits strong agreement with buoy data (bias < 7 cm) and outperforms operational wave models during energetic events. IG wave height estimates also demonstrate good correspondence (MAE $\approx$ 0.9 cm), provided that residual noise amplification is carefully managed. This finding underscores the need for improved noise modelling in future spectral retrieval frameworks. A key uncertainty identified is the role of spatial heterogeneity, which induces representation errors due to the inherent mismatch in spatial and temporal scales between SWOT observations, in-situ buoys, and wave models.
Despite these uncertainties and other limitations, the results confirm SWOT’s capacity to observe nearshore wave dynamics with high spatial detail. The proposed configurations and filtering strategies offer a transferable framework for future applications. These insights support SWOT’s integration into coastal wave model validation, boundary condition improvement, and data assimilation schemes across coastal scales. Ultimately, this thesis advances high-resolution coastal wave monitoring by demonstrating how SWOT’s two-dimensional observations can enhance our understanding of spatial sea state variability, particularly during energetic conditions in morphologically complex environments.
