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北京"23·7"特大洪水复盘分析OA北大核心CSTPCD

Hindcasting on"July 2023"flood event in Beijing

中文摘要英文摘要

本文分别采用地面雨量站和雷达反演降雨数据,利用北京山区洪水预报模型对北京"23·7"暴雨洪水开展了复盘分析.结果表明,雷达反演降雨与地面雨量站测量结果一致性较强,可较好地反映降雨的时空变异性;同时,利用二者分别驱动水文模型后所得的预报效果也基本一致,说明在水文预报工作中雷达反演降雨可作为地面站网的可靠替代品.本文改进的考虑北京山区产流特点的水文模型可对大部分预报断面做出较高精度的模拟.北京山区水文过程具有很强的非线性,基于不同量级历史洪水率定的水文模型参数具有不确定性,为了适应产汇流和洪水演进规律的变化,提高洪水预报的可靠性,预报实践时需要结合实况数据及时优化模型参数,完善洪水预报方案.

In this study,we apply the Beijing flood forecast model and both the gauge-measured and radar-monitored rainfall data to reassess the"July 2023"flood event that occurred in the key regions of Beijing.Results reveal that the radar-derived rainfall data closely align with ground observations,offering a more nuanced representation of the rainfall's temporal and spatial variations.Comparative evaluation of the forecasting capabilities based on these rainfall datasets demonstrates their substantial equivalence,affirming the radar data's viability as a credible alternative to ground measurements.Our specialized Beijing flood forecast model,meticulously tailored to the distinctive runoff characteristics of the city's mountainous areas,consistently exhibits a high accuracy across a wide range of scenarios.The intricate hydrological processes in the city's mountainous terrains are inherently nonlinear;the parameters of its hydrological model,often derived from the historical floods of varying magnitudes,inherently harbor uncertainties.Recognizing the dynamic nature of runoff and flood events,we emphasize the necessity of proactive model parameter optimization.This optimization procedure should integrate real-time conditions and the most current data so as to bolster the reliability of flood predictions.

麦合木提·图达吉;田富强;童瑞;徐宝宁;周睿杨;龚傲凡;曾净;季明峰;戚友存;倪广恒

清华大学 水圈科学与水利工程全国重点实验室,北京 100084||清华大学 水利水电工程系,北京 100084||清华大学 水利部水圈科学重点实验室,北京 100084北京市水文总站,北京 100089中国科学院 地理科学与资源研究所 陆地水循环及地表过程重点实验,北京 100101

地球科学

洪水复盘洪水预报"23·7"特大洪水雷达反演降雨

flood hindcastingflood forecasting"July 2023"floodradar retrieval rainfall

《水力发电学报》 2024 (004)

12-22 / 11

国家重点研发项目(2022YFC3002900);清华大学—宁夏银川水联网数字治水联合研究院项目(SKL-IOW-2022TC2010)

10.11660/slfdxb.20240402

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