As climate change exacerbates water-related hazards in rural and urban areas, the need for robust, flexible solutions to mitigate risks and enhance resilience has become increasingly urgent. Traditional ‘grey’ infrastructure has long dominated flood risk management; however, nature-based solutions (NBS) are gaining traction due to their adaptability, multifunctionality, and ability to provide co-benefits. This study quantified the effectiveness of retention ponds as NBS for reducing flood hazard and risk under current and future climate conditions, employing adaptive pathways and tipping point frameworks for implementing NBS measures in response to climate change. This was applied in Tamnava Basin, Serbia, using a three-step approach: development of future sub-daily rainfall depth–duration–frequency curves (DDF), NBS performance assessment and identification of tipping points and development of adaptive pathways. Coupling HEC-HMS and HEC-RAS models with GIS tools, the study estimated reductions in flood area, volume, and damage costs by 20–27%, 28–35%, and 40–47%, respectively, over the period from the present to 2100, depending on the retention pond configurations. Different adaptive pathway maps were developed, for rainfall return periods. These maps provide decision-makers with flexible, actionable options for implementing NBS measures, bridging the gap between short-term evaluations and long-term climate uncertainties.

The impacts of climate change are becoming more widespread across the world, with hydro-meteorological extreme events on the rise, causing severe threats to nature and communities. Increasing trends in the frequency and intensity of floods and landslides have been projected by climate models. This necessitates the development of more effective measures such as nature-based solutions (NBS) which can complement grey infrastructures. Recent studies have identified
knowledge gaps and limitations in existing research and tools that aid in spatial planning for the implementation of large-scale NBS and proposed new methodologies for the spatial allocation of largescale NBS for flood risk reduction. This work presents a novel method for mapping the suitability of NBS addressing geo-hydrological hazards such as shallow landslides, debris flow, and rockfall, which are typically caused due to slope instability. This methodology incorporates landslide susceptibility mapping, and was used to create a toolbox ESRI ArcGIS environment to aid decision-makers in the planning and implementation of large-scale NBS. The spatial allocation toolbox was applied to the case study Portofino promontory, Liguria region, Italy, and 70% of the area was found to be highly susceptible to landslides. The produced suitability maps show that 41%, 33%, and 65% of the study
area is suitable for the restoration of terraces, bio-engineering, and vegetative measures such as NBS for landslide 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.