期刊
LANDSLIDES
卷 12, 期 3, 页码 437-454出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s10346-014-0493-x
关键词
Debris flow; Susceptibility assessment; Physical models; Statistical model; Pyrenees
资金
- European Community [FP7-ENV-2008-1-226555]
- Spanish Ministry of Education [CGL 2009-13039, CGL2011-23300]
A debris flow is an erosional process that frequently occurs in areas with steep slopes. Although the initiation can be triggered in higher and inhabited altitudes, the propagation and then the deposition can endanger urbanized areas. Frequently, the zones of generation of debris flows are placed in unattainable territories complicating field observations. Moreover, the rapid vegetation's recovery can hide the scars and tracks created by the occurrence of the flow, unlikely to be recognizable in aerial photos. As a consequence, the assessment of debris-flow susceptibility at regional scale is increasing in interest. Two main techniques can be applied for the regional susceptibility assessment: physical models and statistical models. Here, two physical models are introduced simulating the initiation of debris flow by means of shallow landslides: one based on the lateral groundwater flow and the other on vertical groundwater flow. The presented models' algorithms are implemented in a toolbox based on GIS environment and declared as open source code. Being applied in a basin of the Spanish Pyrenees where soil properties and dataset of observations have been gathered, the two models have been evaluated. Successively, they have been compared with a statistical model based on a logistic regression. The results have been aggregated in terrain units, built as fluvio-morphological sub-catchments. Each model has been tested based on the gathered observations. It has been observed that the vertical flow-based model enhances by 18 % the performance of the lateral flow-based model in matching debris-flow susceptible areas. Furthermore, the vertical flow-based model performance is found to be similar to that of the logistic regression, having an averaged quality index of about 70 %.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据