Water quality assessment using satellite-derived chlorophyll-a within the European directives, in the southeastern Bay of Biscay.

The implementation of water quality European Directives requires an intensification of water quality monitoring, within the limits of the Exclusive Economic Zone. Remote sensing technologies can provide a valuable tool for frequent, synoptic, water-quality observations, over large areas. The aim of this study is to assess the ecological status of Basque coastal water bodies using satellite imagery from MODIS sensor, together with optical and chlorophyll-ain situ measurements. Thus, sea surface satellite-derived chl-a algorithms, the OC3 M, OC5 and a Local empirical algorithm, were compared against in situ measurements using satellite in situ match-ups, 90th Percentile (P90) monthly values for the 2005-2010 period. The OC5 algorithm corresponded most accurately with in situ measurements performed in the area, hence, it was selected. A P90 chlorophyll-a map was created with this algorithm to apply the classification scheme required by the directives. The classification of water bodies, based upon satellite-derived chlorophyll-a, could improve considerably the assessment of water quality.

Simulating the migration of drifters deployed in the Bay of Biscay, during the Prestige crisis.

A main conclusion following the oil spill from the Prestige tanker was that improvements in ocean circulation models were necessary; this was in order to predict, more accurately, the trajectories followed by the oil slicks and hence assist in fight against oil pollution operations. In this contribution, the results of the validation of a semi-empirical ocean circulation model, parameterised for the Bay of Biscay and forced with operational oceano-meteorological remote sensing observations, are shown. The model results have been validated with observations from drifting buoys, deployed in the Bay of Biscay during the crisis. The results show that the model explains a relatively large percentage of the current variability. The comparisons between the real and the estimated drifter trajectories indicate that for 3, 5 and 7 day-long trajectories, the drifter position is estimated with errors of approximately 23, 35 and 46km, respectively. The model reproduces relatively well the trajectory followed by the drifter with the shortest period (23 days).