No abstract

For the Netherlands, accurate water level forecasting in the coastal region is crucial, since large areas of the land lie below sea level. During storm surges, detailed and timely water level forecasts provided by an operational storm surge forecasting system are necessary to support, for example, the decision to close the movable storm surge barriers in the Eastern Scheldt and the Rotterdam Waterway. In the past years, a new generation operational tide-surge model (Dutch Continental Shelf Model version 6) has been developed covering the northwest European continental shelf. In a previous study, a large effort has been put in representing relevant physical phenomena in this process model as well as reducing parameter uncertainty over a wide area. While this has resulted in very accurate water level representation (root-meansquare error (RMSE) ∼7-8 cm), during severe storm surges, the errors in the meteorological model forcing are generally non-negligible and can cause forecast errors of several decimetres. By integrating operationally available observational data in the forecast model by means of real-time data assimilation, the errors in the meteorological forcing are prevented from propagating to the hydrodynamic tide-surge model forecasts. This paper discusses the development of a computationally efficient steady-state Kalman filter to enhance the predictive quality for the shorter lead times by improving the system state at the start of the forecast. Besides evaluating the model quality against shelf-wide tide gauge observations for a year-long hindcast simulation, the predictive value of the Kalman filter is determined by comparing the forecast quality for various lead time intervals against the model without a steady-state Kalman filter. This shows that, even though the process model has a water level representation that is substantially better than that of other comparable operational models of this scale, substantial improvements in predictive quality in the first few hours are possible in an actual operational setting.

We present an efficient and flexible alternative method to connect islands and offshore tide gauges with the height system on land. The method uses a regional, high-resolution hydrodynamic model that provides total water levels. From the model, we obtain the differences in mean water level (MWL) between tide gauges at the mainland and at the islands or offshore platforms. Adding them to the MWL relative to the national height system at the mainland’s tide gauges realizes a connection of the island and offshore platforms with the height system on the mainland. Numerical results are presented for the connection of the Dutch Wadden islands with the national height system (Normaal Amsterdams Peil, NAP). Several choices of the period over which the MWLs are computed are tested and validated. The best results were obtained when we computed the MWL only over the summer months of our 19-year simulation period. Based on this strategy, the percentage of connections for which the absolute differences between the observation- and model-derived MWL differences are cm is about 34% (46 out of 135 possible leveling connections). In this case, for each Wadden island we can find several connections that allow the transfer of NAP with (sub-)centimeter accuracy.

The first objective of this paper is to assess by means of geodetic network analyses the impact of adding model-based hydrodynamic leveling data to the Unified European Leveling Network (UELN) data on the precision and reliability of the European Vertical Reference Frame (EVRF). In doing so, we used variance information from the latest UELN adjustment. The model-based hydrodynamic leveling data are assumed to be obtained from not-yet existing hydrodynamic models covering either all European seas surrounding the European mainland or parts of it that provide the required mean water level with uniform precision. A heuristic search algorithm was implemented to identify the set of hydrodynamic leveling connections that provide the lowest median of the propagated height standard deviations. In the scenario in which we only allow for connections between tide gauges located in the same sea basin, all having a precision of 3 cm, the median of the propagated height standard deviations improved by $$38\%$$ 38 % compared to the spirit leveling-only solution. Except for the countries around the Black Sea, coastal countries benefit the most with a maximum improvement of $$60\%$$ 60 % for Great Britain. We also found decreased redundancy numbers for the observations in the coastal areas and over the entire Great Britain. Allowing for connections between tide gauges among all European seas increased the impact to 42%. Lowering the precision of the hydrodynamic leveling data lowers the impact. The results show, however, that even in case the assumed precision is 5 cm, the overall improvement is still 29%. The second objective is to identify which tide gauges are most profitable in terms of impact. Our results show that these are the ones located in Sweden in which most height markers are located. The impact, however, hardly depends on the geographic location of the tide gauges within a country.

Within the hydrodynamic modelling community, it is common practice to apply different modelling systems for coastal waters and river systems. Whereas for coastal waters 3D finite difference or finite element grids are commonly used, river systems are generally modelled using 1D networks. Each of these systems is tailored towards specific applications. Three-dimensional coastal water models are designed to model the horizontal and vertical variability in coastal waters and are less well suited for representing the complex geometry and crosssectional areas of river networks. On the other hand, 1D river network models are designed to accurately represent complex river network geometries and complex structures like weirs, barrages and dams. A disadvantage, however, is that they are unable to resolve complex spatial flow variability. In real life, however, coastal oceans and rivers interact. In deltaic estuaries, both tidal intrusion of seawater into the upstream river network and river discharge into open waters play a role. This is frequently approached by modelling the systems independently, with off-line coupling of the lateral boundary forcing. This implies that the river and the coastal model run sequentially, providing lateral discharge (1D) and water level (3D) forcing to each other without the possibility of direct feedback or interaction between these processes. An additional disadvantage is that due to the time aggregation usually applied to exchanged quantities, mass conservation is difficult to ensure. In this paper, we propose an approach that couples a 3D hydrodynamic modelling system for coastal waters (Delft3D) with a 1D modelling system for river hydraulics (SOBEK) online. This implies that contrary to off-line coupling, the hydrodynamic quantities are exchanged between the 1D and 3D domains during runtime to resolve the real-time exchange and interaction between the coastal waters and river network. This allows for accurate and mass conserving modelling of complex coastal waters and river network systems, whilst the advantages of both systems are maintained and used in an optimal and computationally efficient way. The coupled 1D-3D system is used to model the flows in the Pearl River Delta (Guangdong, China), which are determined by the interaction of the upstream network of the Pearl River and the open waters of the South China Sea. The highly complex upstream river network is modelled in 1D, simulating river discharges for the dry and wet monsoon periods. The 3D coastal model simulates the flow due to the external (ocean) periodic tidal forcing, the salinity distribution for both dry and wet seasons, as well as residual water levels (sea level anomalies) originating from the South China Sea. The model is calibrated and its performance extensively assessed against field measurements, resulting in a mean root mean square (RMS) error of below 6% for water levels over the entire Pearl River Delta. The model also represents both the discharge distribution over the river network and salinity transport p...