山洪灾害临界雨量确定方法对比研究——以雅安名山河流域为例.

Evidence suggests that the critical rainfall thresholds are among the most important factors for flash flood early warnings. In this article, Mingshan River of Ya'an is taken as an example for the contrastive analysis of four rainfall thresholds quantitative approaches, three data-driven statistical inductive approaches, and a hydrologic-dynamic approach are employed in this article to analyze the rainfall threshold of flash flood disaster of various rainfall duration(1, 3, 6, 12, 24 h). The results indicate that the single gauge thresholds approach applies to the scenarios with even-distributed and dense rain gauges. The regional thresholds approach employs areal precipitation instead of single gauge, is applicable to the scenarios with an inadequate amount of gauges, and the thresholds given by this approach are relatively low. The common frequency approach quantifies rainfall thresholds takes a long series of historic gauge data as the sample, and uses probability density functions to obtain the rainfall thresholds linked to the specific frequencies of local flash floods. The results given by the common frequency approach suggest that the fitting curve given by generalized extreme value distribution is optimal, while logarithmic normal distribution and Gumbel distribution are suboptimal. The simple data requirements are the distinctive advantage of this approach and could be applied in scenarios with inadequate flash flood records and rain gauges, but the shortcoming is that the quantified thresholds tend to be artificially high while the frequencies of flash floods are underestimated. Practically, this shortcoming could be reduced by bringing in appropriate attenuation coefficients. The hydrologic-dynamic approach, namely Flood Area hydro-model, applies to the refined simulation of the flash flood life cycle process of the small-sized montane basin. The shortcoming of this approach is that the calculated lowest early warning threshold tends to be overestimated and flash floods are triggered by higher precipitations. In general, thresholds given by data-driven statistical inductive approaches tend to be overestimated, while thresholds given by hydrologic-dynamic approach tend to be underestimated, and multi-methods examinations and modifications are imperative in practice. [ABSTRACT FROM AUTHOR]