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T4-A7 开发极端降雨和洪涝灾害的综合预测能力.pdf

上传人: 表表 编号:1152756 2026-02-14 14页 2.23MB

1、OFFICIALOFFICIALOFFICIALT4-A7 Developing an integrated predictive capability for extreme rainfall and inundation(June 2025 Update)OFFICIALDr.Carlos Velasco-Forero,Paul Fox-Hughes,Jayaram Pudashine,Dragana Zovko-Rajak,Wendy Sharples,Jiawei Hou,Foad Brakhasi,Carla Mooney,David Wilke,Karen Hudson,Victo

2、ria Heinrich,Navid Ghajarnia,Christopher Pickett-Heaps,This research project is funded and supported by Natural Hazards Research Australia.Gillieston Heights is isolated by the rising waters of Wallis Creek.Photo courtesy Eugene Koen(Maitland and District Historical Society,2022)OFFICIALOFFICIAL2Fla

3、sh Flooding Preparedness,Response,Forecast,Warning and CommunicationPart of a collection of research projects funded by Natural Hazards Research Australia(NHRA)and the Bureau of Meteorology(the Bureau)focused on flooding preparedness,response,forecast,warnings and communication.T2-A2 Flash flooding

4、case studies to improve predictions and the communication of uncertaintyT3-A4 Long-Range Flood Outlook for Strategic PreparednessT4-A7 Developing an integrated predictive capability for extreme rainfall and inundationreview mechanisms which may lead to heavy rainfall and subsequent flash floodingass

5、ist understanding of the reasons for potential uncertainty in those forecasts and warnings and enhance the efficiency of communicationexamine the physical science context of a series of events,including the mesoscale meteorology,model short-term rainfall and potential to predict land surface inundat

6、ionWallis Creek July 2022,Hobart-May 2018,Adelaide Nov 2023OFFICIALOFFICIAL3Our ApproachDeveloping an integrated predictive capability for extreme rainfall and inundation(project NHRA T4-A7)Flash flooding:rapid onset speed(within minutes to hours),short-term duration(hours to a day),very localised(u

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1. **项目目标**:开发极端降雨与内涝的集成预测能力(NHRA T4-A7),提升洪水预警与沟通效率。 2. **核心方法**: - **高分辨率集合预报**:使用Bureau的ACCESS-CE模型(1.5公里分辨率,6小时更新)评估降雨不确定性。 - **雷达实时临近预报**:基于STEPS系统(5分钟更新)生成未来几小时降雨集合预测。 - **机器学习内涝预测**:采用LSTM模型快速生成水位及内涝地图,对比Copernicus卫星数据验证(如Wallis Creek案例)。 3. **案例研究**: - **Wallis Creek洪水(2022年7月)**:全球模型预报差异大,ACCESS-CE集合预报与雷达临近预报显著提升局部预测精度。 4. **下一步**:评估Hobart(2018)、Adelaide(2023)等事件,接入新一代ACCESS-AE模型,优化ML/DL与集合预报的集成应用。
**洪水预测如何?** **暴雨预警可靠吗?** **淹没地图准确吗?**
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