The CoasTER geographic database provides erosion relevant characteristics for Europe’s coastal floodplains. It builds on earlier erosion research and includes a coastal geomorphological typology incorporating modification in the form of hard engineering and infrastructure. Analysis shows 27 % of coastal floodplains have shown significant erosion over the last 40 years. Nearly 3,500 km will require additional or new management to protect developed areas and infrastructure if the trend continues.

Small islands face growing threats from climate change. In this context, we consider it important to explore new modeling approaches to improve climate risk assessments in these islands. Our study propose a probabilistic approach using Bayesian Networks (BNs). We developed a expert-based BN model to assess the risk to habitability on four atoll islands in the Indian and Pacific Oceans. The findings suggests that BNs could be used to assess climate-related risk and other adaptation problems.

Developing robust protocols to design multi-model ensembles is of primary importance for the uncertainty quantification of sea level projections. Here, we set up a series of computer experiments to reflect design decisions for the prediction of future sea level contribution of the Greenland ice sheet. We show the importance of including the most extreme climate scenario, and the benefit of having diversity in numerical models for ice sheet modelling and regional climate assessments.

This paper assesses coastal adaptation governance by examining socio-economic and political contexts, reviewing policy frameworks, and identifying challenges. Results show that regional and basin-scale frameworks lack sea level rise provisions, but significant national progress is observed. The main governance challenges are time horizons and uncertainty, coordination, and social vulnerability. These, however, can be addressed if flexible planning and nature-based solutions are implemented.

To improve the interpretability of process-based projections of the sea-level contribution from land ice components, we apply the machine-learning-based SHapley Additive exPlanations approach to a subset of a multi-model ensemble study for the Greenland ice sheet. This allows us to quantify the influence of particular modelling decisions (related to numerical implementation, initial conditions, or parametrisation of ice-sheet processes) directly in terms of sea-level change contribution.