The application of a radar-based depth inversion method to monitor near-shore nourishments on an open sandy coast and an ebb-tidal delta

Coastal management in the Netherlands has the aim to defend coastal zones by preventing flooding and mitigating erosion. To that end, large-scale nourishments are placed in the nearshore, which are supposed to dynamically preserve the coastal zone over a timescale of years. To assess their effectiveness, these nourishments are monitored over large areas and long durations. As repetitive, in-situ measurements become too expensive, remote sensing offers an attractive alternative, mapping depth and near-surface current fields via depth inversion algorithms (DIA). However, the information that can be derived from remotely-sensed data is subject to improvement. In this study a 3D-FFT based DIA named XMFit (X-Band Matlab Fitting) is introduced, which is robust, accurate and fast enough for operational use. Focusing on depth estimates, the algorithm was validated for two case studies in the Netherlands: (1) the “Sand Engine”, a beach mega nourishment at a uniform open coast, and (2) the tidal inlet of the Dutch Wadden Sea island Ameland, characterizing a more complex coast. Considering both sites, the algorithm performance was characterized by a spatially averaged depth bias of −0.9 m at the Sand Engine and a time-varying bias of approximately -2 – 0 m at the Ameland Inlet. When compared to in-situ depth surveys the accuracy was lower, but the time resolution higher. Depth estimates from the Ameland tidal inlet were produced every 50 min by an operational system using a navigational X-Band radar to monitor the placement of a 5 million m³ ebb-tidal delta nourishment – a pilot measure for coastal management. Volumetric changes in the nourishment area over the year 2018, occurring at 7 km distance from the radar, were estimated with an error of 7%. Depth errors statistically correlated with the direction and magnitude of simultaneous near-surface current estimates. Additional experiments on Sand Engine data demonstrated that depth errors may be significantly reduced using an alternative spectral approach and/or by using a Kalman filter.