Abstract

Identifying where nature-based solutions should be prioritized has become a critical task for climate-adaptive urban stormwater management under the combined pressures of climate change and urban expansion. Taking the central urban area of Beijing as a case study, this study develops a dynamic prediction framework that incorporates the Source–Flow–Sink (SFS) process of urban waterlogging. The framework integrates a future land use simulation model (FLUS), the Soil Conservation Service (SCS) hydrological model, and the Maximum Entropy (MaxEnt) model and incorporates both climate change (RCP8.5) and urban expansion to simulate the spatial configuration of waterlogging risk in 2031. High-risk areas were then overlaid with land-cover data and open-space distribution to identify potential NbS opportunity spaces, which were further examined through field investigation. The results show that future waterlogging risk in Beijing exhibits a clear corridor-oriented pattern closely associated with transportation infrastructure. Transportation-related variables account for more than 80% of total model contribution, suggesting a strong statistical association between future waterlogging occurrence and transportation-related spatial features. Field investigation further reveals that many roadside green spaces are elevated above adjacent roads, limiting their ability to receive and retain runoff. Thus, the key adaptation challenge lies not simply in the amount of green space, but in the weak hydrological connection between runoff pathways and adjacent open spaces. While Beijing’s priority areas are mainly corridor-based, other cities may be shaped by different processes and spaces. More broadly, this study demonstrates how hydrological risk simulation can be translated into spatially explicit planning priorities and more locally grounded adaptation decisions.

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