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城市场次降雨情景库构建及预报降雨匹配技术

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[目的]城市降雨具有高度的不确定性和时空变异性,现阶段洪涝风险预测、应急响应预案的时效性和有效性有待加强。为提高"预报降雨—灾害风险识别—应急响应预案"的时效性和有效性[方法]提出了一种基于自组织特征映射网络的数据密集型城市场次降雨情景库构建方法和基于动态时间弯曲法的预报降雨与情景库场次降雨匹配技术。以北京城市副中心通县站 1980-2015 年(36 年)的小时降雨监测数据作为情景库数据基础,并以同地区 2023 年降雨数据作为"预报降雨"进行匹配方法验证。[结果]结果显示:构建的城市场次降雨情景库可全面、有效的反映研究区域降雨特征。"预报降雨"匹配效果良好,平均纳什系数达到0。73,降雨总量平均相对误差为0。17,降雨量峰值平均相对误差为0。09。[结论]该方法可快速实现实时预报降雨与情景库中场次降雨的匹配,通过情景库中预置洪涝风险及应急预案知识库,实现城市洪涝风险的有效识别和及时响应。
Construction of urban rainfall scenario database and matching technology for predicting rainfall
[Objective]Urban rainfall exhibits high uncertainty and spatiotemporal variability,and the timeliness and effectiveness of flood risk prediction and emergency response plans need to be enhanced.To improve the timeliness and effectiveness of"rain-fall forecasting-disaster risk identification-emergency response plan,[Methods]a data-intensive urban sub-hourly rainfall scenar-io library construction method based on Self-Organizing Maps and a forecast rainfall matching technique with dynamic time war-ping method for scenario rainfall library were proposed.Hourly rainfall monitoring data from Tongxian Station in Beijing Urban Sub-center from 1980 to 2015(36 years)were used as the basis for the scenario library data,and rainfall data for the same area in 2023 were used to validate the matching method for forecast rainfall.[Results]The result showed that the constructed urban sub-hourly rainfall scenario library could comprehensively and effectively reflect the rainfall characteristics in the study area.The matching effect of"forecast rainfall"was good,with an average Nash coefficient of 0.73,an average relative error of rainfall total amount of 0.17,and an average relative error of rainfall peak value of 0.09.[Conclusion]This method can quickly achieve real-time matching of forecast rainfall with scenario rainfall in the library.With pre-setting flood risk and emergency plan knowledge in the scenario library,effective identification of urban flood risk and timely response can be achieved.

rainfall patterndynamic time warpingurban water loggingprecipitation forecastrisk assessmentfloodsprecipi-tationclimate change

杨子昕、王佳、刘家宏、王浩、梅超、李峰平

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地下水资源与环境教育部重点实验室,吉林 长春 130021

吉林省水资源与水环境重点实验室,吉林 长春 130021

吉林大学 新能源与环境学院,吉林 长春 130021

中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室,北京 100038

水利部数字孪生流域重点实验室,北京 100038

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雨型 动态时间弯曲 城市洪涝 预报降雨 风险评估 洪水 降水 气候变化

2024

水利水电技术(中英文)
水利部发展研究中心

水利水电技术(中英文)

CSTPCD北大核心
影响因子:0.456
ISSN:1000-0860
年,卷(期):2024.55(10)