AUTHOR=He Na , Fu Qixuan , Zhong Wei , Yang Zhiquan , Cai XianQing , Xu Linjuan TITLE=Analysis of the formation mechanism of debris flows after earthquakes – A case study of the Legugou debris flow JOURNAL=Frontiers in Ecology and Evolution VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2022.1053687 DOI=10.3389/fevo.2022.1053687 ISSN=2296-701X ABSTRACT=

By means of the Graphic Method, Gray Correlation Method and Frequency Method, the total amount of loose solid materials, the amount of loose solid materials per unit area in the source area, and the risk degree and critical rainfall of the study area are determined, respectively. On the basis of the calculation results, the total amount of loose solid materials is 57.2 × 104 m3, the amount of loose solid materials per unit area is 0.13 m3/m2 (greater than 0.1 m3/m2), which better meets the initiation conditions for dilute debris flow. The Hazard Evaluation Model of Debris Flow in earthquake areas is established by the gray correlation method and the hazard index H = 0.725 is determined. According to the hazard classification standards for debris flows in earthquake areas, the debris flow in Lecugou was moderate after the earthquake. The critical rainfall value of 1H, calculated by the frequency method, ranged from 17.45 to 22.21 mm (Re = 25 mm) and 8.17 to 13.01 mm (Re = 50 mm). The critical rainfall value at 10 min ranged from 6.23 to 8.44 mm (Re = 25 mm) and 2.92 to 5.13 mm (Re = 50 mm), respectively. The maximum rainfall intensity of 45.5 mm/h was reached between 10:00 and 11:00 on July 29, 2019 and the cumulative rainfall reached 144 mm, far exceeding the critical rainfall of Legugou. Under the influence of earthquakes and human activities, the loose solid source amount and loose solid source amount per unit area increased greatly, and the critical rainfall for debris flow after earthquakes decreased sharply, which induced debris flows under the influence of heavy rainfall. This research will be helpful for the establishment of monitoring and early warning systems based on artificial intelligence methods, and can greatly improve the effectiveness of disaster prevention and mitigation.