Typhoons are recurring meteorological phenomena in the southeastern coastal area of China, frequently triggering debris flows and other forms of slope failures that result in significant economic damage and loss of life in densely populated and economically active regions. Accurate prediction of typhoon-triggered debris flows and identification of high-risk zones are imperative for effective risk management. Surprisingly, little attention has been devoted to the construction of physical vulnerability curves in typhoon-affected areas, as a basis for risk assessment. To address this deficiency, this paper presents a quantitative method for developing physical vulnerability curves for buildings by modeling debris flow intensity and building damage characteristics. In this study, we selected the Wangzhuangwu watershed, in Zhejiang Province of China, which was impacted by a debris flow induced by Typhoon Lekima on August 10, 2019. We conducted detailed field surveys after interpreting remote sensing imagery to analyze the geological features and the mechanism of the debris flow and constructed a comprehensive database of building damage characteristics. To model the 2019 debris flow initiation, entrainment, and deposition processes, we applied the Soil Conservation Service-Curve Number (SCS-CN) approach and a two-dimensional debris flow model (FLO-2D). The reconstructed debris flow depth and extent were validated using observed debris flow data. We generated physical vulnerability curves for different types of building structures, taking into account both the degree of building damage and the modeled debris flow intensity, including flow depth and impact pressure. Based on calibrated rheological parameters, we modeled the potential intensity of future debris flows while considering various recurrence frequencies of triggering rainfall events. Subsequently, we calculated the vulnerability index and economic risk associated with buildings for different frequencies of debris flow events, employing diverse vulnerability functions that factored in uncertainty in both intensity indicators and building structures. We observed that the vulnerability function utilizing impact pressure as the intensity indicator tends to be more conservative than the one employing flow depth as a parameter. This comprehensive approach efficiently generated physical vulnerability curves and a debris flow risk map, providing valuable insights for effective disaster prevention in areas prone to debris flows.
Understanding the migration and dispersion of potentially toxic elements (PTEs) from soil erosion is integral to managing mine site risks. Modeling PTEs distribution in this mass movement is a significant challenge. This research quantitatively analyzed the ecological-health risks associated with rainfall-induced soil erosion (debris flow) at the LaRonde Mine's tailing pile (Quebec, Canada). The present study adopted a comprehensive approach by employing rheological and infiltration models to correlate ecological-health indices with slurry distribution and calculated various indices to assess five PTEs (As, Cu, Ni, Pb, and Zn). Exposure values, hazardous quotients, and hazard index assessments were used to examine the potential risks to human health. The outcomes show that Zn presents the highest risk, followed by Cu, while As, Ni, and Pb pose no risk. Moreover, the toxic risk index and pollution load index exhibit lower accuracy than other indices. The computed results were visualized using Arc Geographic Information System (ArcGIS), providing a location-dependent risk level distribution and maps of contamination levels. This study demonstrates the importance of quantitative analysis in mine sites' ecologicalhealth risk assessment and provides a framework for developing soil conservation and management strategies at mine sites.