The compounding effects of hydrometeorological hazards are being driven by climate change. As urban areas expand, this leads to degradation of the surrounding environment and exposes more people to hazards. Growing losses show that conventional approaches to addressing these issues can compound these problems. Over the last few decades, nature-based solutions (NBSs) have become an increasingly popular alternative. These measures, inspired by natural processes, have shown potential for reducing hazards by complementing traditional approaches and providing co-benefits in the form of eco-system services. With the adoption of NBSs becoming a more mainstream approach, there is a need for tools that support the planning and implementation of interventions. Geospatial suitability assessment is a part of this planning process. Existing tools are limited in their application for large-scale measures. This paper intends to improve this by building upon a multi-criteria analysis (MCA)-based approach that incorporates biophysical and land use criteria and conditions for mapping the suitability of large-scale NBSs. The methodology was developed and tested on six sites to assess the suitability of floodplain restoration, retention or detention, afforestation, and forest buffer strips. The resulting suitability maps also show potential for combining two or more measures for greater risk reduction.

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.