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Nature-based solutions are increasingly suggested for mitigating coastal flood risks in the face of climate change. Managed realignment (MR), a coastal adaptation strategy that entails the landward realignment of coastal defences to restore coastal habitats (often salt marshes), plays a pivotal role in implementing nature-based solutions in the coastal zone. Across Europe, more than 130 sites have been implemented so far, often to harness their potential to mitigate coastal flood risks while restoring coastal habitats (ABPmer, 2021). However, local communities often oppose MR projects, not only because they are seen as returning hard-won land to the sea but also because their coastal protection function is less trusted than traditional hard engineering techniques. This scepticism has foundation. The proclaimed coastal protection function of MRs is based on a broad body of literature on the protective function of natural salt marshes. However, contrary to natural salt marshes, MRs are often semi-enclosed tidal basins with narrow breaches to the open sea/estuary. Recent studies indicate that MR-internal hydrodynamics may significantly reduce their coastal protection, depending on their engineering design. To successfully implement MR, a much-improved scientific knowledge base is needed, as well as a process for addressing community concerns and genuinely engaging stakeholders in decision-making beyond the usual obligatory consultancy approach. Here, we propose the co-production of scientific knowledge with local communities and stakeholders to optimize the success of coastal nature-based solutions and promote community acceptance.

This study explores the uncertainty introduced in global assessments of coastal flood exposure and risk when not accounting for water-level attenuation due to land-surface characteristics. We implement a range of plausible water-level attenuation values for characteristic land-cover classes in the flood module of the Dynamic and Integrated Vulnerability Assessment (DIVA) modelling framework and assess the sensitivity of flood exposure and flood risk indicators to differences in attenuation rates. Results show a reduction of up to 44 % in area exposure and even larger reductions in population exposure and expected flood damages when considering water-level attenuation. The reductions vary by country, reflecting the differences in the physical characteristics of the floodplain as well as in the spatial distribution of people and assets in coastal regions. We find that uncertainties related to not accounting for water attenuation in global assessments of flood risk are of similar magnitude to the uncertainties related to the amount of sea-level rise expected over the 21st century. Despite using simplified assumptions to account for the process of water-level attenuation, which depends on numerous factors and their complex interactions, our results strongly suggest that an improved understanding and representation of the temporal and spatial variation of water levels across floodplains is essential for future impact modelling.

The geosciences are a highly suitable field of application for optimizing model parameters and experimental designs especially because many data are collected. In this paper, the weighted least squares estimator for optimizing model parameters is presented together with its asymptotic properties. A popular approach to optimize experimental designs called local optimal experimental designs is described together with a lesser known approach which takes into account the potential nonlinearity of the model parameters. These two approaches have been combined with two methods to solve their underlying discrete optimization problem. All presented methods were implemented in an open-source MATLAB toolbox called the Optimal Experimental Design Toolbox whose structure and application is described. In numerical experiments, the model parameters and experimental design were optimized using this toolbox. Two existing models for sediment concentration in seawater and sediment accretion on salt marshes of different complexity served as an application example. The advantages and disadvantages of these approaches were compared based on these models. Thanks to optimized experimental designs, the parameters of these models could be determined very accurately with significantly fewer measurements compared to unoptimized experimental designs. The chosen optimization approach played a minor role for the accuracy; therefore, the approach with the least computational effort is recommended.