基于位移比模型的三峡库区云阳县域内蠕变型滑坡降雨预警

建立高效合理的区域滑坡灾害降雨预警模型对滑坡防治具有重要意义.然而以往的研究多侧重于临滑预警，对蠕变型滑坡在强降雨工况下的短暂加速变形的预警研究还有待深入.以三峡库区云阳县域内滑坡为例，首先根据滑坡地表位移监测数据的特点对统计样本进行合理筛选.再通过降雨因子与滑坡发生的相关性分析以及对滑坡在降雨条件下位移变化情况的数值模拟，确定了适用于不同时间阶段的降雨统计变量.然后将考虑了滑坡规模特征的滑坡位移比（累计位移与滑坡纵长之比）作为变形指标，分时段统计滑坡地表位移监测数据与历史降雨信息，建立了日降雨数据与月位移数据的对应关系，得到了可用于确定降雨量阈值的位移比模型，并获得了云阳县蠕变型滑坡的五级预警分区.最后分别选用研究区滑坡险情实例、长年位移监测数据及极端降雨事件对模型预警效果进行检验.结果显示基于专业监测数据的位移比模型的滑坡降雨预警结果与实际情况相符，可为蠕变型滑坡的预警预报提供依据. 

Rainfall Warning of Creeping Landslide in Yunyang County of Three Gorges Reservoir Region Based on Displacement Ratio Model

Establishing the efficient and reasonable regional landslide hazards rainfall warning model plays an important role in landslide prevention. However, more previous studies have been focused on the early warning before sliding rather than on the temporary accelerated deformation of creeping landslide after the heavy rainfall. Taking landslides in Yunyang County of the Three Gorges Reservoir region as an example, statistical samples were selected reasonably according to the characteristics of the landslide surface displacement monitoring data firstly. Then the rainfall statistical variable applied to different time periods was determined by correlation analysis between rainfall index and landslide occurrence, and numerical simulation of displacement change of landslides in the rainfall condition. The displacement ratio (ratio of cumulative displacement and length of landslide) considering the scale characteristic of landslide was selected as the landslide deformation index and monitoring data of landslides and historical rainfall information in different time periods were counted. The relationship between daily rainfall data and monthly displacement data was built up. The displacement ratio model which can determine the rainfall thresholds was obtained, and fivelevel warning partition of creeping landslides in Yunyang County was determined. Finally, landslide instances, long-time displacement monitoring data and extreme rainfall event in research area were used to test the warning results of this model, respectively. The results show that the warning results of this model based on monitoring system data were the same as real conditions, and it could provide the basis for the early warning and prediction of the creeping landslides.