How do urban river networks regulate city climate? A case study in Shanghai, China

Urban blue (water) and green (vegetation) spaces are natural refuges of cool spots for citizens to escape from the extreme heat outdoors and have been widely used in traditional and modern urban designs called ‘water towns’ (i.e., buildings are sited along rivers and trees), particularly in Southern China with rich water resources. This study represents the first comprehensive investigation into the cooling effect of urban river networks at different climatic scales in Shanghai, a Chinese megacity characterized by a significant presence of water towns. At the neighborhood scale, we conducted fine-resolution street-level monitoring of microclimatic data along various rivers during the 2022 heatwave periods in central Shanghai and applied an advanced spatial regression algorithm to quantify the synergistic effect of river and vegetation. At the city scale, we quantified the cooling buffer zones and cooling intensities of urban river networks by integrating fine-resolution urban river network maps with multi-source remotely sensed datasets. We found that the width of rivers, coverage ratio, density, and morphology of river networks are the key factors affecting the cooling potential. The confluence or proximity of river tributaries can also bring an enhanced cooling effect than standalone ones. In a diurnal cycle, rivers can lead to an averaged cooling intensity of 0.4–0.8 °C in air temperature with a maximum value of 3.5 °C in the afternoon, as well as a cooling distance ranging from 100 m to 700 m at various riverside neighborhoods. On the other hand, city-scale results show that river networks can provide a considerable cooling buffer zones covering 36.9% of Shanghai and a maximum cooling intensity of 5.5 °C in surface temperature. Our study implies that urban river networks cannot be neglected in urban climatic studies and should be incorporated into a new conceptualization of water-included urban local climate zone classifications in the world urban database.