Fig. 7.4.1: Water level distribution (input signal: 3 positive single signals, wave height 5 m, 
from the north, North Sea 2 km) 
Left: after 1.5 hours with realistic depth distribution (T 1800 s) 
Flight: after 0.5 hour with constant water depth (h 500 m, T 600 s) 
54 
7.4 North Sea signal from the north 
Figs. 7.4.1-6 show, in their left part, the propagation of the boundary signal described in 
section 7.1 (three positive signals, 71800 s, H 5 m). For better physical understanding, 
computations were additionally carried out with a constant water depth of 500 m in the entire 
North Sea. In these simulations, the same kind of boundary signals were used, but a period 
of 600 s. 
7.4.1 Propagation 
The influence of realistic bottom topography is obvious from the faster propagation in deep 
water, in this case the Norwegian Trench. In the north, the simulation shows major local 
water level maxima (Figs. 7.4.1 and 7.4.2, left figures). They are caused by directional 
changes and subsequent superposition among the single signals due to an inhomogeneous 
depth gradient (refraction). These maxima do not occur in the comparative computations with 
a flat bottom (right figures). 
Diffraction and reflection produce locally very high water levels in bays and estuaries, and 
near islands (e.g. Lerwick station, Fig. 7.1.3). Such processes are governed by the shape of 
the coastline and the location of islands. They may, however, be subject to modification by 
variable bottom topography, which influences the direction and velocity of propagation, cf. 
Figs. 7.4.2 and 7.4.3, left figures. 
The German coast is located in the wave shadow of Norway. It is reached first by extensions 
of the input signal that has weakened on the shelf (Fig. 7.4.4). Later, a secondary signal 
generated by superposition of a diffraction pattern also arrives at the coast (Fig. 7.4.6). It is 
generated mainly by diffraction and reflection on the British coast. It is better visible in the 
flat-bottom simulations but also constitutes a significant signal in model time series using a 
realistic topography. 
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