Jianqi Zhuang

Probability prediction model for landslide occurrences in Xi’an, Shaanxi Province, China

Landslides are increasing since the 1980s in Xi’an, Shaanxi Province, China. This is due to the increase of the frequency and intensity of precipitation caused by complex geological structures, the presence of steep landforms, seasonal heavy rainfall, and the intensifcation of human activities. In this study, we propose a landslide prediction model based on the analysis of intraday rainfall (IR) and antecedent effective rainfall (AER). Primarily, the number of days and degressive index of the antecedent effective rainfall which affected landslide occurrences in the areas around Qin Mountains, Li Mountains and Loess Tableland was established. Secondly, the antecedent effective rainfall and intraday rainfall were calculated from weather data which were used to construct critical thresholds for the 10%, 50% and 90% probabilities for future landslide occurrences in Qin Mountain, Li Mountain and Loess Tableland. Finally, the regions corresponding to different warning levels were identified based on the relationship between precipitation and the threshold, that is; “A” region is safe, “B” region is on watch alert, “C” region is on warning alert and “D” region is on severe warning alert. Using this model, a warning program is proposed which can predict rainfall-induced landslides by means of real-time rain gauge data and real-time geo-hazard alert and disaster response programs. Sixteen rain gauges were installed in the Xi’an region by keeping in accordance with the regional geology and landslide risks. Based on the data from gauges, this model accurately achieves the objectives of conducting real-time monitoring as well as providing early warnings of landslides in the Xi’an region.

Identification of landslide spatial distribution and susceptibility assessment in relation to topography in the Xi＇an Region, Shaanxi Province, China

Landslides are among the most serious of geohazards in the Xi’an Region, Shaanxi, China, and are responsible for extensive human and property loss. In order to understand the distribution of landslides and assess their associated hazards in this region, we used a combination of frequency analysis, logistic analysis, and Geographic Information System (GIS) analysis, with consideration of the spatial distribution of landslides. Using the GIS approach, the five key factors of surface topography, including slope gradient, topographic wetness index (TWI), height difference, profile curvature and slope aspect, were considered. First, the distribution and frequency of landslides were considered in relation to all of the five factors in each of three sub-regions susceptible to landslides (Qin Mountain, Li Mountain, and Loess Tableland). Secondly, each factor’s influence was determined by a logistic regression method, and the relative importance of each of these independent variables was evaluated. Finally, a landslide susceptibility map was generated using GIS tools. Locations that had recorded landslides were used to validate the results of the landslide susceptibility map and the accuracy obtained was above 84%. The validation proved that there is sufficient agreement between the susceptibility map and existing records of landslide occurrences. The logistic regression model produced acceptable results (the areas under the Receiver Operating Characteristics (ROC) curve were 0.865, 0.841, and 0.924 in the Qin Mountain, Li Mountain and Loess Tableland). We are confident that the results of this study can be useful in preliminary planning for land use, particularly for construction work in high-risk areas.

Liquefaction of loess landslides as a consequence of irrigation

Most landslides on the Loess Tableland (Shaanxi, China) are of the sliding-flow type, with long run-out distances. These landslides, which tend to be triggered by irrigation, result in loss of life and damage to infrastructure. In order to reveal the characteristics and mechanisms of sliding-flow landslides, field investigations, surveys and indoor tests, including consolidated undrained triaxial tests and ring-shear tests, were conducted. The apparent friction of sliding-flow loess landslides triggered by irrigation in the study area ranged from 0.01 to 0.25, with a mean of 0.159. These values are lower than those associated with the landslides triggered by the Wenchuan Earthquake. The deviation stress decreased to about 99% of its peak value, while the path of the effective stress revealed that the effective normal stress approached zero. Consequently, saturated loess samples were highly liquefiable under consolidated undrained triaxial tests. The pore pressures increased sharply and up to their peak value (mean of 130 kPa), corresponding to 92% of the applied normal stress. The path of effective stress did not reach the failure line. The loose structure and macro-pore content of loess provide the framework for static liquefaction to occur as a consequence of the generation of fine particles.