This modeling study evaluates the impact of urban blue spaces on air temperature regulation in cities, suggesting improvements for heat management.
Cities in northwestern Europe face increasingly extreme summer heat under climate change, intensifying the need for effective neighbourhood‐scale heat mitigation strategies. Using hectometric (100 m) idealized Weather Research and Forecasting (WRF) simulations during three extreme heat events, this study examines how urban blue space configuration, atmospheric forcing, and physical mechanisms regulate air temperature and thermal comfort (wet‐bulb globe temperature index) across coastal and inland cities. We assess how surface energy fluxes interact with horizontal advection to propagate cooling beyond waterbodies, while evaluating whether WRF‐Lake produces physically realistic outputs for small, shallow urban blue spaces. Our simulations show near‐surface horizontal advection as the dominant cooling mechanism, mixing cooler air from blue spaces with warmer urban air. Around midday, this provides approximately cooling potential, amplified by evaporative cooling enhanced by urban‐generated turbulence. Daily mean temperature reductions ranged from 0.1°C to 0.4°C, with peak morning effectiveness reaching 1.0°C in coastal areas. Wind speed emerged as the primary control: moderate winds (4.7–) propagated cooling citywide, extending up to three times the city diameter downwind, whereas light winds () limited cooling locally. Randomly distributed waterbodies created more homogeneous cooling than canal configurations. Thermal comfort analysis revealed a critical temperature–humidity trade‐off. Factor analysis () showed air temperature cooling (50.3%) is counteracted by increased relative humidity (42.3%). We identified limitations of WRF‐Lake for shallow urban blue spaces. Default roughness lengths underestimate turbulence and fluxes, likely underestimating cooling and causing unrealistic water temperature increases. This underscores the need for improved parametrizations and targeted observations to advance urban hydrometeorological modelling.
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Chen et al. (2026) studied this question.
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