Process-based sediment mapping – an alternative for dynamic and data-scarce coasts

• Efficacy of process-based mapping in dynamic data-scarce coasts is evaluated. • Accumulated sediment flux (ASF) based on gross bed-material exchange is introduced. • ASF closely follows observed sediment patterns best in dynamic channels and flats. • Implications for habitat assessment and coastal management are discussed. Information about the spatial distribution of seabed sediments is fundamental for habitat assessment and coastal management. However, sediments are highly variable in space and time, depending on geological preconditions, sediment sources, hydrodynamic drivers, and human influence. In practice, sediment distribution maps must often rely on sparse opportunistic samples, which are interpolated using geostatistical or machine-learning techniques. These approaches may be inadequate in dynamic data-scarce environments where none or low-resolution sampling, due to inaccessibility, cannot capture transient event-derived changes in the seabed. For such environments, this study investigates the ability of process-based numerical modelling approaches to produce sediment distributions under representative hydrodynamic conditions and the underlying morphology in predominantly sand environments. The North Frisian Wadden Sea (NFWS) is used as an example of a dynamic coastal system of very high ecological importance. Two process-based approaches are compared: the Bed Composition Generator (BCG) and a novel Accumulated Sediment Flux (ASF) approach, and their performance is evaluated against observed data and a data-based Co-Kriging (CoK) benchmark. The findings indicate that, when started without prior sediment data, ASF tends towards a fine bias, whereas BCG tends towards a coarse bias. BCG shows low variability in the predicted distribution, whereas ASF produces more realistic sorting patterns while conforming more closely to the underlying morphology. ASF performs particularly well over the flats and approaches the skill of the data-based benchmark. However, point-wise spatial analysis identified local mismatch clusters around relict sediments, indicating that such methods are most effective in dynamic areas with recent sediment cover and should be applied more cautiously in stable, low-energy settings. The results further show that representative forcing conditions are important for predictive skill and that partial prior sediment information can substantially reduce systematic biases in both process-based methods. Overall, these findings show that process-based mapping, especially ASF, can provide reliable sediment distribution data in dynamic zones, predominantly sandy environments where sediment data are sparse or absent, thereby supporting habitat assessment and coastal management.