RESUMEN
The Alderney Race is assumed to have the largest tidal-stream energy potential in the north-western European coastal seas. Interaction of the powerful tidal stream with strong wind, high waves and irregular bathymetry creates hydrodynamic conditions of extreme complexity, with high levels of turbulence. A comprehensive dataset has been created to improve the understanding of physical processes, turbulence, tidal stream and resource variability at the site. The database contains a large amount of oceanographic and meteorological measurements acquired in Alderney Race in 2017-2018. This exceptionally long period of observations (nearly one year) became possible due to modern tools and strategies of data acquisition. The paper presents some significant results from the database analysis. Among many results, we would like to underline the following: (i) a wide range of variability of mean flow and sea state parameters was documented; (ii) exceptionally large values of current velocity (7 m s-1) and significant wave height (8 m) were measured during extreme meteorological conditions; (iii) high-frequency variability of current speed during storm events was also found to be very large, with the standard deviation of velocity reaching 0.3 m s-1 in the bottom boundary layer, and 0.6 m s-1 in the surface layer; and (iv) predominant wind and wave direction relative to the flow impacts the wave height and significantly increases the turbulence kinetic energy of the flow. To our knowledge, this is the largest multi-variable database available on potential tidal energy sites. The results of database analysis can represent a significant advance in environmental conditions and resource characterization and provide advanced information to turbine developers. This article is part of the theme issue 'New insights on tidal dynamics and tidal energy harvesting in the Alderney Race'.
RESUMEN
To characterize a sedimentary environment, it is risky to take a single sample when the spatial variability is unknown. A reference station has to reflect the natural variations in order to allow the creation of long time series. However, it can remain unclear whether the temporal changes are real or due to a spatial variation. We highlight here the importance of spatial variability at the scale of precision of the GNSS. It appears that the number and arrangement of replicates depend on the environment and the studied parameters. InC, TOC and TS show a sufficiently low spatial variability to allow temporal tracking using GNSS without multiplying samples. The fine fraction percent shows a high spatial variability over small distances. The study of this parameter in the framework of temporal tracking requires a knowledge of its spatial variability during each period of sampling, and hence leads to the multiplication of samples.