Department Urban Water Management

Cities are increasingly adopting blue-green infrastructure (BGI) to address the dual challenges of extreme rainfall and rising temperatures driven by climate change. While the potential of BGI for urban stormwater management is well-studied, the cooling effect of stormwater-focused BGI remains underexplored. This study investigates the heat mitigation potential of three stormwater BGI elements, bioretention cells, porous pavements, and detention ponds, within three urban street canyons in a Swiss town near Zurich. The Urban Tethys-Chloris (UT&C) microclimate model was modified to explicitly represent stormwater BGI and assess their influence on the Universal Thermal Climate Index (UTCI) at 2 meters above the ground. Simulations were conducted under both historical climate and a future climate projection, including a sensitivity analysis of soil types. BGI can cool up to 2.7 °C, but their effectiveness depends on the type of BGI, the surface it replaces, the time of the day, and the availability of water. Soil properties were found to significantly influence the cooling effect of bioretention cells, with finer-textured soils achieving higher soil moisture levels and greater reductions in UTCI. A trade-off between cooling and stormwater infiltration also emerged. Sandy soils favor infiltration but dry out quickly, limiting cooling, while clay-rich soils limit infiltration but retain moisture and sustain evaporative cooling, even under future climate conditions with longer dry spells. These findings highlight the importance of integrating hydrological and thermal considerations into BGI design. Integrated approaches that balance both objectives are needed.

Urban climates typically exhibit high local variability and diverge markedly from mesoclimate conditions. Yet, urban microclimate data needed to assess the impacts of urban heat on biodiversity are lacking. We present a scalable modeling approach to generate high-resolution projections of urban bioclimatic conditions using Zurich, Switzerland, as a case study. We demonstrate that globally available land use and remote sensing data can effectively predict spatial patterns in urban bioclimatic conditions. Our findings show that mean annual temperatures can deviate by up to 2 °C from mesoclimate conditions, and that uncorrected mesoclimate data substantially underestimate risks to 182 species of amphibians, birds, butterflies, dragonflies, grasshoppers and trees. By the end of the century, up to half of these species are expected to exceed their climatic tolerance across all habitat patches. Our approach is transferable to cities globally using existing data, marking a major step forward for urban microclimate and biodiversity research.

Understanding how the performance of blue-green infrastructure (BGI), nature-based stormwater management systems such as bioretention cells and green roofs, evolves is essential to sustaining their hydrological function. However, traditional monitoring approaches typically compare BGI performance using rainfall events classified as similar based solely on total depth, which can obscure subtle differences in hydrological performance. This study presents a workflow that improves temporal assessment by (i) identifying statistically similar rainfall events across periods using rainfall characteristics such as intensity, duration, and peaking time, and (ii) analysing hydrological response using both volume- and time-based metrics.
We evaluated the workflow under controlled but synthetic conditions using simulations of bioretention systems with underdrains. Two Swiss case studies (Kloten and Bern) were used to represent rainfall-only and mixed inflow BGI configurations, respectively.
Results show that isolating temporally similar rainfall events reduces variability compared with volume-based grouping, enabling more reliable detection of performance change. Among the performance metrics tested, the volume-based indicator proved more robust, detecting temporal differences in 16–38 matched rainfall–response pairs for the two configurations. In the mixed inflow system, additional runoff increased variability, but the workflow still detected a consistent performance change over time. In contrast, time-based metrics such as P-UF lag and centroid shift required substantially larger samples to achieve comparable statistical power.
These findings demonstrate the workflow's value as a proof-of-concept for designing efficient BGI monitoring programmes. Applications to other BGI types, climatic regimes, and extended datasets remain future work.