Directional wave measurements using the Offshore Sensing Sailbuoy, link to MATLAB file

An autonomous vessel, the Offshore Sensing Sailbuoy, was used for wave measurements near the Ekofisk oil platform complex in the North Sea (56.5 N, 3.2 E, operated by ConocoPhilllips) from 6 to 20 November 2015. Being 100% wind propelled, the Sailbuoy has two-way communication via the Iridium network and has the capability for missions of six months or more. It has previously been deployed in the Arctic, Norwegian Sea and the Gulf of Mexico, but this was the first real test for wave measurements. During the campaign it held position about 20km northeast of Ekofisk (on the lee side) during rough conditions. Mean wind speed measured at Ekofisk during the campaign was near 9.8m/s, with a maximum of 20.4m/s, with wind mostly from south and south west. A Datawell MOSE G1000 GPS based 2Hz wave sensor was mounted on the Sailbuoy. Mean significant wave height (Hs 1hr) measured was 3m, whereas maximum Hs was 6m. Mean wave period was 7.7s, while maximum wave height, Hmax, was 12.6m. These measurements have been compared with non-directional Waverider observations at the Ekofisk complex. Mean Hs at Ekofisk was 3.1m, while maximum Hs was 6.5m. Nevertheless, the correlation between the two measurements was high (97%). Spectra comparison was also good, except for low Hs (~1m), where the motion of the vessel seemed to influence the measurements. Nevertheless, the Sailbuoy performed well during this campaign, and results suggests that it is a suitable platform for wave measurements in rather rough sea conditions.

Results of wave modelling for Moreton Bay, Southeast Queensland, and a reef lagoon off Lizard Island, Great Barrier Reef, Australia

The distribution, abundance, behaviour, and morphology of marine species is affected by spatial variability in the wave environment. Maps of wave metrics (e.g. significant wave height Hs, peak energy wave period Tp, and benthic wave orbital velocity URMS) are therefore useful for predictive ecological models of marine species and ecosystems. A number of techniques are available to generate maps of wave metrics, with varying levels of complexity in terms of input data requirements, operator knowledge, and computation time. Relatively simple "fetch-based" models are generated using geographic information system (GIS) layers of bathymetry and dominant wind speed and direction. More complex, but computationally expensive, "process-based" models are generated using numerical models such as the Simulating Waves Nearshore (SWAN) model. We generated maps of wave metrics based on both fetch-based and process-based models and asked whether predictive performance in models of benthic marine habitats differed. Predictive models of seagrass distribution for Moreton Bay, Southeast Queensland, and Lizard Island, Great Barrier Reef, Australia, were generated using maps based on each type of wave model. For Lizard Island, performance of the process-based wave maps was significantly better for describing the presence of seagrass, based on Hs, Tp, and URMS. Conversely, for the predictive model of seagrass in Moreton Bay, based on benthic light availability and Hs, there was no difference in performance using the maps of the different wave metrics. For predictive models where wave metrics are the dominant factor determining ecological processes it is recommended that process-based models be used. Our results suggest that for models where wave metrics provide secondarily useful information, either fetch- or process-based models may be equally useful.