Perspectives on Target-Oriented NbS/BGI Interventions through Integrated Hydrodynamic Modeling and Social Indicators

As climate change intensifies, urban areas are increasingly exposed to compound hydro-climatological extremes, including heat waves, pluvial flooding, droughts, and associated thermal and health stresses. These hazards are interconnected, and their impacts are modulated by socio-demographic factors (e.g. age structure, population density), urban form (e.g. imperviousness, ventilation corridors), and pre-existing environmental burdens such as air pollution and noise. Understanding these impact chains is essential for identifying where adaptation measures can most effectively reduce vulnerability.

Nature-based Solutions (NbS), including unsealing, green infrastructure, and decentralized stormwater retention, have demonstrated substantial potential to simultaneously mitigate heat and flood impacts. Despite their proven benefits, implementation in Berlin remains fragmented and spatially limited, with measures typically realized as isolated projects rather than strategically located where their effectiveness would be highest.

This study identifies priority locations for NbS implementation by integrating multi-criteria indicators: pluvial flood risk, environmental burdens (air pollution, noise, thermal stress), and socio-demographic development. Using population projections for Berlin until 2040 combined with the Environmental Justice Atlas, the Friedrichshain district was identified as exhibiting elevated vulnerability to hydro-climatological extremes.

The district is characterized by continued population growth (+2.1 % by 2040), the highest projected increase in average age across Berlin (from 38.9 to 41.6 years), high surface sealing (~70 %), limited green infrastructure, and pronounced thermal load. These characteristics make the district particularly susceptible to compound flood and heat hazards and a representative case for highly built-up environments.

Building-scale pluvial flood risk was assessed using the 2D shallow water model hms⁺⁺, simulating a 100-year precipitation event (48.8 mm in 1 hour). Mesh resolutions of 2×2 m, 4×4 m, and 8×8 m were compared to analyze flood extent and volumes while balancing model precision and computational efficiency. Flooding hotspots were identified using the unsupervised clustering algorithm DBSCAN, enabling robust detection of clusters with varying shapes and densities. Results reveal major flooding clusters in the south-western study area, particularly at sealed crossroads with limited infiltration capacity and high cumulative environmental burdens. Initial scenario analyses demonstrate that selective unsealing of public spaces (schoolyards, parking areas) can substantially reduce total flood volume (-6 % at 8×8 m; -44 % at 2×2 m) and inundated area (-4 % at 8×8 m; -35 % at 2×2 m) in the largest clusters.

Future work will incorporate time-varying infiltration and evapotranspiration schemes to capture wetting-drying cycles, vegetation dynamics, cooling effects of blue-green infrastructure, and potential drought stress. The proposed framework supports integrated assessment of flood-heat-drought interactions and provides evidence-based guidance for climate adaptation strategies in vulnerable urban districts.