As a 2D model inherently also simulates sea level variations, it was possible to validate the model against the RDCP measured HSP phosphorylation sea level variations

as well ( Figure 4c). As a rule, proper hydrodynamic models do not need calibration, but the results can be controlled somewhat by the choice of coastline, bathymetry, cross-sections of the straits and wind input ( Suursaar et al. 2002). We used un-modified Kihnu wind data, which represent the marine wind conditions over the Gulf of Riga, but may slightly overestimate the winds over the Väinameri. Bearing in mind further long-term hindcasts and the limited availability of hourly sea level data from earlier periods, we compared the simulations with the hourly sea level forcings taken from the Ristna tide gauge and interpolated from monthly average Ristna sea levels. The differences in cumulative current velocity components

were surprisingly small ( Figure 5a). The rather similar behaviour of the curves being compared can be explained by the use of integral data, where short-term fluctuations cancel each other out. Also, the study area is located in the central part of the model domain, where the high-frequency impulses of the boundary sea level conditions propagating from both the Irbe Strait and the Väinameri side meet each other. This means that the information carried http://www.selleckchem.com/products/mitomycin-c.html by the high resolution wind forcing is the most important for currents ( Otsmann et al. 2001), and low-frequency variations in boundary sea level are sufficient. Within the semi-enclosed sub-basins, their own sea level patterns are created by the model. Unlike the 2D model, the SMB-type wave model is not a true hydrodynamic model and the results can be controlled (calibrated) somewhat by the depth-parameter, but more importantly by the choice of fetch lengths.

Our calibrations included the depth-parameter of 19 m for Kõiguste and 21 m for Matsi. By trying to keep the maximum and average wave heights equal in the modelled and measured these series (Figure 5b,c), which covered 40 days of hourly data at Kõiguste and 60 days at Matsi, maximizing the correlation coefficient and minimizing the RMSE, the best sets of fetches were obtained separately for Kõiguste and Matsi. Afterwards, using wind forcing from the same source (i.e. the Kihnu station) and the same fetches, long-term (1966–2011) wave hindcasts were calculated. Because of the regular shape of the Gulf of Riga and the near absence of remotely generated wave components from the Baltic Proper, the calibrations were equally successful at Kõiguste and Matsi. Some mismatch between the measured and modelled time series (Figure 5) was due to a temporal shift during strong wind events, and also as a result of local small-scale wind events, which do not spread over the 35–55 km distances between the wind forcing and modelling sites.