Adjusting for Measurement Lag in the Estimation of Rapid Marine Tidal Current Flow with Sparse Spatiotemporal Boat Survey Data

Eric P.M. Grist and Jason D. Mc Ilvenny
Environmental Research Institute (ERI) North Highland College, UHI Millennium Institute Thurso, Caithness, UK
{Eric.Grist, Jason.Mcilveny}@thurso.uhi.ac.uk

Abstract: Accurate determination of marine tidal current flow is a crucial component in assessing offshore site suitability for marine renewable energy devices. The use of real time boat surveys to provide field data of estimated current speeds and directions can help identify potential sites for development. However, in a rapidly changing tidal flow system the time lag between measurements at different points in space and time may induce significant error into estimates of current speeds and directions. This in turn implies substantial inaccuracies from spatiotemporal computations which attempt to extrapolate tidal flow patterns over a wider regional scale. Whereas hydrodynamic models can provide flow parameter estimates at any position and time, they cannot be relied on to be accurate without verification or correction from such field measurements. Here, we introduce a statistical approach which adjusts for measurement lags inherent in measured flows, by combining boat survey data with hydrodynamic model output. The approach exploits a regression model which is fitted to observed differences between survey data and output from the hydrodynamic model. It is exemplified with the POLPRED® (POLPRED, 2007) hydrodynamic model for the Pentland Firth in the north of Scotland, a key region designated for development of tidal stream technology. The regression model is used to estimate currents with associated uncertainties over a spatiotemporal range within the domain of the survey region. Our results indicate that correction for measurement lag is likely to be a major factor in achieving accurate estimation of currents in such dynamic marine environments.

Keywords: regression; current profile; hydrodynamic model; ADCP; POLPRED

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