四川省滑坡灾害气象预警模型建立与验证

四川省滑坡灾害严重,特别是2008年之后,灾情显著加剧,如何预防滑坡灾害是保护人民生命财产安全的有效途径。滑坡灾害的预警模型研究是滑坡灾害预防领域的核心课题。本文对四川省滑坡灾害危险性进行了评价,并开展了滑坡灾害气象风险预警模型研究。①以确定性系数的方法量化坡度、地形起伏度、水文地质岩性、植被覆盖度、地震烈度和年均降雨量因子,建立逻辑回归模型,定量地进行四川省滑坡灾害危险性区划,并对结果进行验证。结果表明,四川省滑坡灾害高危险性区域成“Y”字型分布,此外川中、川东北地区滑坡灾害危险性也非常高,这与四川省滑坡灾害的空间分布情况相符。②在前期滑坡灾害与降雨量统计分析、滑坡灾害危险性评价的基础上,以滑坡灾害危险性评价为静态因子,日降雨量数据为动态因子,通过逻辑回归模型的结果,确定以当日降雨量概率化值、滑坡灾害危险性值、前一日降雨概率化值、前两日降雨概率化值、前三日降雨概率化值为临灾模型影响因子,各因子对预警结果影响程度按上述顺序递减,建立了地质-气象耦合的临灾气象预警模型。通过检验区数据对模型的检验表明,该预警模型能成功预警80%以上的滑坡灾害;通过滑坡灾害群发个例检验发现,该预警模型与四川省现用模型相比,预警区域明显减小,空报率和漏报率显著降低。

Establishment and Validation of a Meteorological Warning Model for Landslide Hazards in Sichuan Province

Landslide disaster is serious in Sichuan province. This influence is more obvious after the year 2008. How to prevent landslide disaster is an effective way to reduce landslide disaster losses and protect people's lives and property. Research on early warning models of landslide hazard is the core issue of landslide disaster prevention. This study collected the landslide data, precipitation data between 2008 and 2013, digital elevation data, geological lithology data and seismic intensity data. Our research can be divided into the following two parts: (1) Evaluation of landslide hazard in Sichuan Province. This study used the method of deterministic coefficient to quantify the slope, relief amplitude, hydrogeological lithology vegetation coverage, seismic intensity and annual rainfall factor. We also established a logistic regression model to quantitatively analyze the risk of landslide disaster in Sichuan Province. The results were also verified. The results indicated that the high risk area of landslide disaster in Sichuan is similar to the shape of the letter "Y". The risk value of landslide disaster is as high as 0.97. In addition, the risk of landslide disaster in northeastern Sichuan is very high, with a maximum of 0.8. According to the statistical analysis of the frequency of landslide and the analysis of risk zoning area, the area of region where the value of landslide hazard is between 0.1 and 0.2 accounted for 22% of the whole province's area. The area of the risk value exceeding 0.9 occupies only 5% of the area of the whole province. 35% of the historical disaster points are located in this area, indicating that the degree of landslide disaster risk is high. The spatial distribution characteristics of landslide hazards in Sichuan Province is as follows: the landslide is zonally distributed along the Longmenshan fault zone, the Xianshui River fault zone and the Anning River fault zone, and clustered in the northeastern Sichuan, which is consistent with the model results.(2) Research on early warning model of the meteorological risk of landslide disaster. Based on the statistical analysis of the early landslide disaster and rainfall, and the risk assessment of landslide disaster, this study took the landslide risk assessment as the static factor and the daily rainfall data as the dynamic factor to determine the precipitation probability value, the zoning value of the landslide disaster risk, precipitation probability value of one day ahead, precipitation probability value of two days ahead as the influence factor of the model. The influence degree of each factor to the warning results is decreasing in the order above. Finally, we established the meteorological coupling warning model of the landslide hazards. According to the verification of 2139 disaster points, 80% of the landslide disaster can be successfully predicted, among which 30% of the landslide disaster warning values are more than 0.75. 18% warning values of the landslide disaster are higher than 0.99. 90% of the large and super large landslide disasters can be successfully predicted. 40% of large-scale landslide disaster warning results are greater than 0.75. 12% of large-scale landslide disaster warning results are more than 0.99. On July 10th, 2003, there was a case of group-occurring landslide. It shows that the warning area decreased greatly. Empty quote rate and missing quote rates are greatly reduced compared with the current model results of Sichuan province.