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Catastrophic debris flows triggered by a 14 August 2010 rainfall at the epicenter of the Wenchuan earthquakeAbstract The Wenchuan earthquake of May 12, 2008 produced large amounts of loose material (landslide debris) that are still present on the steep slopes and in the gullies. This loose material creates an important hazard as strong rainfall can cause the development of devastating debris flows that will endanger the resettled population and destroy the result of reconstruction efforts. On 14 August 2010, a total of 21 debris flows were triggered by heavy rainfall around the town of Yingxue, located near the epicenter of the Wenchuan earthquake. One of these debris flows produced a debris dam, which then changed the course of the river and resulted in the flooding of the newly reconstructed Yinxue town. Prior to this catastrophic event, debris flow hazard had been recognized in the region, but its potential for such widespread and devastating impacts was not fully appreciated. Our primary objective for this study was to analyze the characteristics of the triggering rainfall and the sediment supply conditions leading to this event. Our field observations show that even small debris flow catchment areas have caused widespread sediment deposition on the existing fans. It is concluded that the whole of the area shaken by the Wenchuan earthquake is more susceptible to debris flows, initiated by localized heavy rainfall, than had been assumed earlier. The results of this study contribute to a better understanding of the conditions leading to catastrophic debris flow events in the earthquake-hit area. This is essential for the implementation of proper early warning, prevention, and mitigation measures as well as a better land use planning in this area.

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The lack of detailed COVID-19 cases at a fine spatial resolution restricts the investigation of spatial disparities of its attack rate. Here, we collected nearly one thousand self-reported cases from a social media platform during the early stage of COVID-19 epidemic in Wuhan, China. We use kernel density estimation (KDE) to explore spatial disparities of epidemic intensity and adopt geographically weighted regression (GWR) model to quantify influences of population dynamics, transportation, and social interactions on COVID-19 epidemic. Results show that self-reported COVID-19 cases concentrated in commercial centers and populous residential areas. Blocks with higher population density, higher aging rate, more metro stations, more main roads, and more commercial point-of-interests (POIs) have higher density of COVID-19 cases. These five explanatory variables explain 76% variance of self-reported cases using an OLS model. Commercial POIs have the strongest influence, which increase COVID-19 cases by 28% with one standard deviation increase. The GWR model performs better than OLS model with the adjusted R 2 of 0.96. Spatial heterogeneities of coefficients in the GWR model show that influencing factors play different roles in diverse communities. We further discussed potential implications for the healthy city and urban planning for the sustainable development of cities.

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