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Accurate storm surge modeling is essential for predicting coastal flooding and mitigating impacts on vulnerable regions. This study evaluates the influence of different sea surface drag parameterizations on surge predictions using the Global Tide and Surge Model (GTSM) over a 10-year period (2006–2015) and two storm events. Four model experiments were tested, ranging from a fully dynamic formulation, including variable air density, atmospheric stability, and sea-state-dependent drag, to a simplified constant-drag approach. Results show that advanced drag formulations reduced the underestimation of annual maximum surge values from 18% to 12% globally, with the variable Charnock parameter contributing the most. Conversely, using a constant Charnock value and thereby neglecting wave-dependent roughness increases prediction errors, especially in regions with highly variable sea states. Case studies of Storm Xaver (2013) and Hurricane Fiona (2022) show that advanced parameterizations better capture wind stress variations, reducing root mean square error from 0.21 m to 0.16 m for Xaver and improving surge predictions by up to 0.30 m for Fiona. Consistent with earlier studies, a persistent underestimation of extreme surge events remains across all experiments. While wave-dependent roughness improves performance, no single parameter fully explains this bias. However, wave-dependent roughness particularly enhances model performance in high-latitude and storm-prone areas, where sea state and atmospheric conditions vary widely. Our results show that variations in air density and atmospheric stability have minimal impact on surge height. As such, prioritizing the implementation of dynamic, sea-state-dependent drag formulations, particularly variable Charnock, is key to further improving the accuracy of storm surge forecasting systems and future projections.
With the continued rise in global mean sea level, operational predictions of tidal height and total water levels have become crucial for accurate estimations and understanding of sea level processes. The Dutch Continental Shelf Model in Delft3D Flexible Mesh (DCSM-FM) is developed at Deltares to operationally estimate the total water levels to help trigger early warning systems to mitigate against these extreme events. In this study, a regional version of the Empirical Ocean Tide model for the Northwest European Continental Sea (EOT-NECS) is developed with the aim to apply better tidal forcing along the boundary of the regional DCSM-FM. EOT-NECS is developed at DGFI-TUM by using 30 years of multi-mission along-track satellite altimetry to derive tidal constituents which are estimated both empirically and semi-empirically. Compared to the global model, EOT20, EOT-NECS showed a reduction in the root-square-sum error for the eight major tidal constituents of 0.68 cm compared to in situ tide gauges. When applying constituents from EOT-NECS at the boundaries of DCSM-FM, an overall improvement of 0.29 cm was seen in the root-mean-square error of tidal height estimations made by DCSM-FM, with some regions exceeding a 1 cm improvement. Furthermore, of the fourteen constituents tested, eleven showed a reduction of RMS when included at the boundary of DCSM-FM from EOT-NECS. The results demonstrate the importance of using the appropriate tide model(s) as boundary forcings, and in this study, the use of EOT-NECS has a positive impact on the total water level estimations made in the northwest European continental seas.
Dutch coastal policy aims for a safe, economically strong and attractive coast. This is achieved by maintaining the part of the coast that support these functions; the coastal foundation. The coastal foundation is maintained by means of sand nourishments. Up to now, it has been assumed that net transports across the coastal foundation's offshore boundary at the 20 m depth contour are negligibly small. In the framework of the Coastal Genesis 2.0 program we investigated sand transports across this boundary and across other depth contours at the lower shoreface. This paper presents a computationally efficient approach to compute the annual sand transport rates at the Dutch lower shoreface. It is based on the 3D Dutch Continental Shelf Model with Flexible Mesh (3D DCSM-FM), a wave transformation tool and a 1DV sand transport module. We validate the hydrodynamic input against field measurements and present flow, wave and sand transport computations for the years 2013–2017. Our computations show that the net annual sand transport rates along the Dutch coast are determined by peak tidal velocities (and asymmetry thereof), density driven residual flows, wind driven residual flows and waves. The annual mean alongshore transports vary along the continuous 20 m depth contour. The computed total cross-shore transports are onshore directed over the continuous 20 m, 18 m and 16 m depth contours and increase with decreasing water depth. The effect of density difference and wind on the 3D structure of the flow and on the sand transports cannot be neglected along the Dutch lower shoreface. Our computations show that excluding the effect of density results in a significant decrease of the onshore directed transports. Also switching off wind largely counteracts this effect. The net cross-shore transport is determined by a delicate balance between gross onshore and offshore transports, where wave conditions are important. We show an example for Scheveningen where the net cross-shore transport is onshore directed when including all wave conditions but would be offshore directed when excluding waves higher than 3.5 m. In contrast, at Callantsoog the highest waves contribute more to the offshore directed transports. These results suggest that storm conditions play an important role in the magnitude and direction of the net annual transport rates at the lower shoreface.
Removing unwanted signals from satellite observations improves least-squares-based harmonic estimates, given an inversion matrix with the same condition number. This variance reduction is the core of the remove compute restore approach commonly used. First, residual harmonic sets are computed with the difference between observations and model background estimates through conventional or weighted least-squares. Then, the residual harmonics are added to the background model estimates.
Here we implemented a method that extends the typical approach by including model background estimate and error covariance in the least-squares step. This inclusion helps to weigh between constituents well represented in the model and those that must be updated.
To test the method, we designed a semi-synthetic experiment. First, we used tide gauge data to generate a satellite equivalent dataset and compared estimations between the two methods listed above and the model estimate. Next, we applied the method to compute tidal estimates along satellite radar altimeter tracks (T/P Jason) in the 2D Dutch Coastal Shelf Model (DCSMv6) domain.
Results from the synthetic experiment show that the second method produces consistently better estimates reducing RSS consistently through temporal cross-validation. In addition, it provides an effective way of keeping as many constituents estimates as the model series can resolve, adding the benefits of satellite observations. Finally, results from the North Sea implementation show the new estimates increase the variance reduction of satellite residuals across the whole domain relative to background tidal estimates. The range of improvements varies between 0 and 3cm, which is significant given already very accurate model background estimates. The benefited areas include the English Channel, the Irish Sea, the English North-Sea Coast, the Bay of Biscay, the German Bight, and the North Atlantic region close to the upper boundary of the model domain. ...
Removing unwanted signals from satellite observations improves least-squares-based harmonic estimates, given an inversion matrix with the same condition number. This variance reduction is the core of the remove compute restore approach commonly used. First, residual harmonic sets are computed with the difference between observations and model background estimates through conventional or weighted least-squares. Then, the residual harmonics are added to the background model estimates.
Here we implemented a method that extends the typical approach by including model background estimate and error covariance in the least-squares step. This inclusion helps to weigh between constituents well represented in the model and those that must be updated.
To test the method, we designed a semi-synthetic experiment. First, we used tide gauge data to generate a satellite equivalent dataset and compared estimations between the two methods listed above and the model estimate. Next, we applied the method to compute tidal estimates along satellite radar altimeter tracks (T/P Jason) in the 2D Dutch Coastal Shelf Model (DCSMv6) domain.
Results from the synthetic experiment show that the second method produces consistently better estimates reducing RSS consistently through temporal cross-validation. In addition, it provides an effective way of keeping as many constituents estimates as the model series can resolve, adding the benefits of satellite observations. Finally, results from the North Sea implementation show the new estimates increase the variance reduction of satellite residuals across the whole domain relative to background tidal estimates. The range of improvements varies between 0 and 3cm, which is significant given already very accurate model background estimates. The benefited areas include the English Channel, the Irish Sea, the English North-Sea Coast, the Bay of Biscay, the German Bight, and the North Atlantic region close to the upper boundary of the model domain.
The Rhine region of freshwater influence (ROFI) is strongly stratified, rotational, relatively shallow and has large tides, resulting in a dynamic field of fronts that are formed by multiple processes. We use a 3D numerical model to obtain a conceptual picture of the frontal structure and the processes responsible for generating this multiple front structure in the Rhine ROFI. The horizontal salinity gradient and numerical tracers are used to identify three different types of fronts: outer, inner, tidal plume and relic tidal plume fronts. Tidal plume front (TPF) trajectories together with the tracers demonstrate that TPFs exist for longer than one tidal cycle. A Lagrangian frontogenesis analysis shows that the fronts are strengthened mainly as a result of increased convergence, which is observed to occur at times when tidal straining is large. Additionally the alongshore tidal excursion and the dominance of the tidal currents over the intrinsic frontal propagation speed, trap TPFs within 20 km from the river mouth. Trapping and re-strengthening maintain several fronts at a time in the mid-field region, resulting in a multi-frontal system. The observation of a complex river plume system is expected to be important for cross-shore exchange, transport and coastal ecology.
Effects of self-attraction and loading at a regional scale
A test case for the Northwest European Shelf
The impact of the self-attraction and loading effect (SAL) in a regional 2D barotropic tidal model has been assessed, a term with acknowledged and well-understood importance for global models but omitted for boundary-forced, regional models, for which the implementation of SAL is non-trivial due to its non-local nature. In order to understand the impact of the lack of SAL effects in a regional scale, we have forced a regional model of the Northwest European Continental Shelf and the North Sea (continental shelf model (CSM)) with the SAL potential field derived from a global model (GTSM), in the form of a pressure field. Impacts have been studied in an uncalibrated setup and with only tidal forcing activated, in order to isolate effects. Additionally, the usually adopted simple SAL parameterization, in which the SAL contribution to the total tide is parameterized as a percentage of the barotropic pressure gradient (typically chosen 10%), is also implemented and compared to the results obtained with a full SAL computation. A significant impact on M2 representation is observed in the English Channel, Irish Sea and the west (UK East coast) and south (Belgian and Dutch Coast) of the North Sea, with an impact of up to 20 cm in vector difference terms. The impact of SAL translates into a consistent M2 amplitude and propagation speeds reduction throughout the domain. Results using the beta approximation, with an optimal domain-wide constant value of 1.5%, show a somewhat comparable impact in phase but opposite direction of the impact in amplitude, increasing amplitudes everywhere. In relative terms, both implementations lead to a reduction of the tidal representation error in comparison with the reference run without SAL, with the full SAL approach showing further impacted, improved results. Although the overprediction of tidal amplitudes and propagation speeds in the reference run might have additional sources like the lack of additional dissipative processes and non-considered bottom friction settings, results show an overall significant impact, most remarkable in tidal phases. After showing evidence of the SAL impact in regional models, the question of how to include this physical process in them in an efficient way arises, since SAL is a non-local effect and depends on the instantaneous water levels over the whole ocean, which is non-trivial to implement.