The escalating impacts of climate change trigger the necessity to deal with hydro-meteorological hazards. Nature-based solutions (NBSs) seem to be a suitable response, integrating the hydrology, geomorphology, hydraulic, and ecological dynamics. While there are some methods and tools for suitability mapping of small-scale NBSs, literature concerning the spatial allocation of large-scale NBSs is still lacking. The present work aims to develop new toolboxes and enhance an existing methodology by developing spatial analysis tools within a geographic information system (GIS) environment to allocate large-scale NBSs based on a multi-criteria algorithm. The methodologies combine machine learning spatial data processing techniques and hydrodynamic modelling for allocation of large-scale NBSs. The case studies concern selected areas in the Netherlands, Serbia, and Bolivia, focusing on three large-scale NBS: rainwater harvesting, wetland restoration, and natural riverbank stabilisation. Information available from the EC H2020 RECONECT project as well as other available data for the specific study areas was used. The research highlights the significance of incorporating machine learning, GIS, and remote sensing techniques for the suitable allocation of large-scale NBSs. The findings may offer new insights for decision-makers and other stakeholders involved in future sustainable environmental planning and climate change adaptation.

Flood adaptation measures implemented at the household level play an important role in reducing communities' vulnerability. The aim of this study is to enhance the current modelling practices of human-flood interaction to draw new insights for flood risk management (FRM) policy design. The paper presents a coupled agent-based and flood model for the case of Hamburg, Germany, to explore how individual adaptation behaviour is influenced by flood event scenarios, economic incentives and shared and individual strategies. Simulation results show that a unique trajectory of adaptation measures and flood damages emerges from different flood event series. Another finding is that providing subsidies increases the number of coping households in the long run. Households' social network also has a strong influence on their coping behaviour. The paper also highlights the role of simple measures such as adapted furnishings, which do not incur any monetary cost, in reducing households' vulnerability and preventing millions of euros of contents damages. Generally, we demonstrate that coupled agent-based and flood models can potentially be used as decision support tools to examine the role of household adaptation measures in flood risk management. Although the findings of the paper are case-specific, the improved modelling approach shows the potential to be applied in testing policy levers and strategies considering heterogeneous individual behaviours.