Reply on RC1

Abstract

Shallow loess landslides induced by prolonged heavy rainfall are common in loess dominated areas and often result in property loss, human casualties, and sediment pollution. Building a suitable prediction model for shallow landslides in loess areas is critical for landslide mitigation. In 2013, prolonged heavy rains from July 19th to the 25th triggered shallow loess landslides in Tianshui, China. The “7.25 loess landslides” were used as a case study for this current study. Landslide data, along with the characteristics of the loess shallow landslides were obtained through multiple field investigations and remote sensing interpretations. The “7.25 loess landslides" demonstrated clustering, high density, small areas, and long travel distance. The depth of the sliding surface correlates with the saturated layer (i.e., liquid limited water content) arising from rainfall infiltration, with a sliding depth that is typically less than 2 m and is negatively correlated with the slope. Based on the common characteristics of shallow loess landslides, the mechanisms involved in the sliding flow landslide are proposed. The Revised Infinite Slope Model (RISM) was proposed using equal differential unit method and corrected the deficiency that the safety factor increases with the slope increasing when the slope is larger than 50° calculated using the Taylor slope infinite model. The relationship between the critical depth and the slope of the shallow loess landslide was determined and combined with the characteristics of rainfall infiltration. The intensity-duration (I-D) prediction curve of the rainfall-induced shallow loess landslides under different slopes was constructed and can be used in forecasting regional shallow loess landslides. Additionally, the influence of loess strength on the shallow loess landslide stability has been analyzed. The shallow loess landslide stability responds to slope and cohesion but is not sensitive to the internal friction angle.