A regional-scale method of forecasting debris flow events based on water-soil coupling mechanism

• Published in: Journal of mountain science 2014-11, Vol.11 (6), p.1531-1542
• Main Authors: Zhang, Shao-jie, Wei, Fang-qiang, Liu, Dun-long, Yang, Hong-juan, Jiang, Yu-hong
• Format: Article
• Language: English
• Subjects: Debris flow; Detritus; Disasters; Earth and Environmental Science; Earth Sciences; Ecology; Environment; Environmental monitoring; Fluid mechanics; Forecasting; Geography; Landslides & mudslides; Mountains; Research Papers; Towns; Watershed management; Watersheds
• ISSN: 1672-6316; 1993-0321; 1008-2786
• DOI: 10.1007/s11629-013-2906-z

A debris flow forecast model based on a water-soil coupling mechanism that takes the debris-flow watershed as a basic forecast unit was established here for the prediction of disasters at the watershed scale. This was achieved through advances in our understanding of the formation mechanism of debris flow. To expand the applicable spatial scale of this forecasting model, a method of identifying potential debris flow watersheds was used to locate areas vulnerable to debris flow within a forecast region. Using these watersheds as forecasting units and a prediction method based on the water-soil coupling mechanism, a new forecasting method of debris flow at the regional scale was established. In order to test the prediction ability of this new forecasting method, the Sichuan province, China was selected as a study zone and the large-scale debris flow disasters attributable to heavy rainfall in this region on July 9, 2013 were taken as the study case. According to debris flow disaster data on July 9, 2013 which were provided by the geo-environmental monitoring station of Sichuan province, there were 252 watersheds in which debris flow events actually occurred. The current model predicted that 265 watersheds were likely to experience a debris flow event. Among these, 43 towns including 204 debris-flow watersheds were successfully forecasted and 24 towns including 48 watersheds failed. The false prediction rate and failure prediction rate of this forecast model were 23% and 19%, respectively. The results show that this method is more accurate and more applicable than traditional methods.