小流域山洪灾害具有成灾时间短、突发性强、危害性大、频次多等特点,严重威胁着区域内社会经济发展和人民群众生命财产安全。受当前山区雨洪资料少、产汇流机理不够完善所限,小流域的暴雨洪水模拟存在着可靠性差、精度低的问题。以我国中部秦巴山区山洪灾害典型流域官山小流域为研究对象,搜集了近期代表性较好的10场洪水,以TOPMODEL、TUWMODEL和新安江模型结果为基础,基于分流量区间BMA方法计算模型权重,提出适用于小流域的水文集合预报模型。结果显示,相比单一水文模型,分流量区间BMA方法模拟的洪峰流量、洪量和峰现时间指标的合格率可分别提高4.5%、39.7%和48.9%,为官山小流域洪水模拟提供了一种有效的模拟手段。研究成果对提高山区小流域洪水预报精度具有一定的理论意义和实践价值。
Abstract
Sudden and frequent mountain torrent disasters in small watershed feature short time and severe hazard, hence posing severe threats to regional economic growth and the safety of people's lives and property. Limited by the lack of rainfall and flood data in mountainous areas as well as the imperfect flow generation and confluence mechanism, the storm and flood simulation for small watershed is subjected to poor reliability and low accuracy. With Guanshan small watershed, a typical basin with mountain flood disaster in the Qinling-Daba Mountains in central China as a case study, we collected the data of ten representative flood events in recent years and simulated the floods with Xin'anjiang model,TUWMODEL,and TOPMODEL,respectively.On the basis of the modelling results, we calculated the weights of the aforementioned models in different intervals by using the BMA (Bayesian model averaging) method and presented a combined hydrological model suitable for small watersheds. Results demonstrated that compared with single hydrological model, the BMA method could enhance the pass rate of simulating flood peak discharge, flood volume and time of peak by 4.5%,39.7%,and 48.9%,respectively.The present research offers an effective approach for simulating the floods in Guanshan small watershed, and meanwhile is of theoretical and practical values for improving the accuracy of flood forecasting in small watershed in mountainous area.
关键词
分流量区间BMA方法 /
新安江模型 /
TUWMODEL /
TOPMODEL /
暴雨山洪 /
洪水模拟 /
官山小流域
Key words
BMA method for dividing flow interval /
Xin'anjiang model /
TUWMODEL /
TOPMODEL /
torrential rain /
flood simulation /
Guanshan small watershed
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基金
科技部重点研发计划项目(2017YFC1502503,2018YFC0407202);国家自然科学基金项目(41890822,51879193)
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