Quantifying unequal urban resilience to rainfall across China from location-aware big data

Abstract

Abstract. Disaster-relevant authorities could make uninformed decisions due to the lack of a clear picture of urban resilience to adverse natural events. Previous studies have seldom examined the near-real-time human dynamics, which are critical to disaster emergency response and mitigation, in response to the development and evolution of mild and frequent rainfall events. In this study, we used the aggregated Tencent location request (TLR) data to examine the variations in collective human activities in response to rainfall in 346 cities in China. Then two resilience metrics, rainfall threshold and response sensitivity, were introduced to report a comprehensive study of the urban resilience to rainfall across mainland China. Our results show that, on average, a 1 mm increase in rainfall intensity is associated with a 0.49 % increase in human activity anomalies. In the cities of northwestern and southeastern China, human activity anomalies are affected more by rainfall intensity and rainfall duration, respectively. Our results highlight the unequal urban resilience to rainfall across China, showing current heavy-rain-warning standards underestimate the impacts of heavy rains on residents in the northwestern arid region and the central underdeveloped areas and overestimate impacts on residents in the southeastern coastal area. An overhaul of current heavy-rain-alert standards is therefore needed to better serve the residents in our study area.