白龙江流域降雨型泥石流活动规模预测模型

Prediction Model of Activity of Rainfall-type Debris Flow in Bailongjiang River Basin

白龙江地区泥石流沟谷内大量松散固体物质在强降雨的激发下易形成规模较大的降雨型泥石流,泥石流一次冲出量比同等条件下要放大数倍,应用以往泥石流活动规模预测模型进行计算的结果与实际值误差较大,因此,需构建适用于白龙江流域暴雨型泥石流一次最大冲出量定量预测方法。以区内降雨频率为10 a一遇的24条沟谷型泥石流历史活动规模为典型实例,结合野外调查,利用MATALB多元非线性统计方法建立泥石流一次最大冲出量预测模型,结合舟曲三眼峪沟“8·8”特大泥石流等5条暴雨型泥石流灾害特征对预测模型优化完善,构建了不同规模降雨频率下的泥石流活动规模定量表达式。结果表明:影响泥石流一次最大冲出量的有泥石流灾害爆发区面积、流域内松散固体物质总量及诱发泥石流的降雨条件等3个因素,所建立的模型适用于白龙江流域降雨沟谷型泥石流活动规模的预测。该方法可为经济建设安全地段选址和未来城镇泥石流快速风险管理提供重要依据。

A large number of loose solids in the valleys of the Bailong River area are prone to form large-scale rainfall-type debris flow under the stimuli of heavy rainfall.Once the debris flow volume is larger than usual,it is magnified by several times.There is a large error between the result and the actual value,when the past debris flow activity scale prediction model is used for calculation.Therefore,it is necessary to construct a quantitative prediction method for the maximum rush-out volume of rainfall-type debris flows in the Bailong River Basin.Taking the historical scale of 24 gully debris flows with the frequency of rainfall of 10 a in the region as a typical example.,combining with field surveys,a multi-variable non-linear statistical method of MATLAB was used to establish a prediction model for the maximum amount of debris flow outbreaks.Five storm-type mudslides,such as the“8.8”large debris flow in the trench,were used to optimize the prediction model.And the quantitative scale expressions of debris flow activity at different scales of rainfall frequency were constructed.The results show that there are three factors that affect the maximum volume of debris flow outbreak,including the area of the debris flow disaster area,the total amount of loose solids in the watershed,and the rainfall conditions of the induced debris flow.The model is applicable to predict the scale of rainfall gully debris flow in the Bailong River Basin.Based upon the forecast,this method can provide an important basis for the economic construction of safe site selection and the future risk management of urban debris flow.