Seeing sense: developing multiple quality control parameters to assist in the interpretation and understanding of X-Band radar derived bathymetry

Quantifying the dynamics of the shallow nearshore is key to creating complete sediment budget models required for developing effective shoreline management strategies (Holman et al., 2015; Kinsela et al., 2022), to protect coastal communities threatened by rising sea levels worldwide. High costs, time-consuming collection and challenging physical conditions for boats and crew are some of the key barriers preventing widespread repeat collection of bathymetric survey data despite its critical importance in understanding coastal change (Román-Rivera and Ellis, 2019). In recent years, X-band radar derived bathymetry has offered an exciting glimpse into nearshore zone seabed changes over large areas at high frequency intervals (Atkinson et al., 2021; Rutten et al., 2017; Williams et al., 2017). This is done through inferring water depths from certain wave parameters (period and wavelength) extracted from the X-band radar imaged sea surface (Bell, 1999; Young et al., 1985). However, as complimentary bathymetry datasets are rarely available there has been limited opportunity to validate and scrutinise the bathymetry inferred from radar. The ‘Delivering Legacy from the Pevensey Bay Coastal Defence Scheme’, was initiated to try and understand why beach material below mean sea level was being lost on one of the UK’s most actively managed beaches (Thomas, 2015). To quantify this a X-band radar system was deployed between November 2020 and February 2022 alongside 12 conventional repeat bathymetric surveys. Following an extensive data comparison exercise we present five quality control (QC) criteria, two of which were recently developed to help understand the complexities of temporal and spatially variable error developed as part of the Radar Research, Results & Data to Decisions (R3D2) project. The comprehensive comparison between the radar-derived bathymetry output and conventionally surveyed bathymetry data highlighted good agreement in ‘intermediate’ depths and divergences in the very shallow and deeper ends of the shoreface (Figure 1). Additionally, the quality of the radar-derived bathymetry was shown to be sensitive to incoming wave angle. The five different QC parameters wave-look-angle, wave-current-alignment, wavelength-depth�saturation, signal intensity and probability density function correlation are described, explaining multiple factors which can affect how effectively the wave-inversion algorithm can perform. Together these QC parameters paint a detailed picture of the waves, currents and bathymetry that are being inferred promoting a better basis for data exclusion before data aggregation and final bathymetric map production (Figure 2). A QC procedure is proposed alongside further discussion on how the QC parameters can be used together, including techniques such as fuzzy-logic. This work provides an important stepping stone in refining radar-derived bathymetric mapping, providing more meaningful data for decision-makers at temporal and spatial scales not previously attainable.