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水利部专题部署太湖流域洪水防御工作_我的网站

敦刻尔克

一 |

Students from Xi'an University of Technology test a virtual reality-enabled emergency evacuation simulation system tailored for flood disasters on January 12, 2024. Photos: Courtesy of Xi'an University of TechnologyEditor's Note:
Extreme weather is increasingly a global challenge, and the key to addressing climate risks lies in earlier prediction, more precise action and smarter preparedness, with emerging technologies playing a vital role. The Global Times launches the "Climate Gambit" series, exploring how research teams are leveraging cutting-edge technologies, including artificial intelligence, high-performance computing and smart observation systems, to anticipate weather changes, enhance disaster early-warning and strengthen resilience against climate risks.
Inside a state key laboratory at Xi'an University of Technology, Northwest China's Shaanxi Province, there is a miniature but complete "water world" which simulated water channels, inland lakes and main rivers to recreate real flood scenarios and test their newly developed GPU Accelerated Surface Water Flow and Transport Model (GAST).
Known as a "super brain" for flood control, GAST can complete flood simulations involving more than 3 million computational units within 30 seconds, helping transform flood management from a reaction to emergency into active precautions since "flooding impacts can be predicted even before rainfall arrives."
At a time when extreme rainfall and summer flooding have become increasingly frequent, questions such as when the flooding will arrive, which roads may be submerged and when residents should evacuate have become increasingly important.
In an exclusive interview with the Global Times, Hou Jingming, a professor at Xi'an University of Technology and the leader of the research team, explained how the GAST model seeks to answer these questions by accurately predicting flood development and identifying vulnerable areas before disasters occur, and how the model helps authorities take preventive measures to reduce casualties and economic losses.
AI empowering 'flood drill'
The water tank system in the lab was designed to create a controllable, repeatable and observable environment to simulate complex hydrological processes, including river flooding, urban water level changes, lake regulation, drainage pump operations and coordinated flood-control measures.
By adjusting variations such as upstream water inflow, rainfall intensity, downstream water levels and drainage conditions, scientists can recreate different flood scenarios. Meanwhile, water levels, flow speeds and other data are collected in real time and displayed on a digital twin platform.
"If a rainstorm and corresponding floods are an exam, GAST is like a 'drill,'" Hou said. "It can simulate how floods develop, where water will flow, which areas may be inundated and when river levels may rise, ensuring authorities are well but not overly prepared."
To answer the public's concern about "whether my neighborhood will be flooded when heavy rain arrives," the team developed new algorithms for urban surface water flow, including improvements in terrain slope and friction calculations.
These breakthroughs have improved simulation accuracy in complex urban environments. Compared with extensive monitoring data, GAST can keep simulation errors of key hydrodynamic factors within 15 percent. This means the model can provide not only general flood trends, but also quantitative information such as water depth, flow speed and inundation areas.
Combined with AI technologies, it can identify complex relationships between rainfall, water conditions, flood depth, flow velocity and affected areas, cutting simulations from hours in traditional methods to minutes or even seconds.
The faster calculation capability means that once meteorological authorities update forecasts, the model can quickly estimate flood risks in different parts of a city.
"The earlier rainfall warnings are issued, the earlier we can identify potential flooding hotspots and high-risk areas," Hou said. "This saves valuable time for evacuation, traffic management and emergency deployment."
For smarter disaster response

Building an accurate flood prediction model also requires integrating large amounts of urban data other than weather forecasts, including urban terrain, drainage networks and infrastructure information.
For example, a model developed for Xi'an incorporates geographic data and drainage system information collected from relevant authorities and field surveys. After receiving rainfall forecasts, the system can quickly calculate possible flooding scenarios, showing when and where waterlogging may occur and highlighting vulnerable roads and areas through visual maps.
To demonstrate how the super brain works in case of possible flooding, the laboratory has set a virtual reality area where visitors can experience a simulated urban flooding evacuation in the Xiaozhai area of Xi'an. Wearing VR headsets, participants can see water levels gradually rising and follow emergency instructions to move toward higher ground.
The entire technological package has already been applied in real-world flood prevention.
A 3D live?scene display lab in Xi'an that oversees stormwater drainage performance in Hengshui, North China's Hebei Province Photos: Courtesy of Xi'an University of Technology

A 3D live-scene display lab in Xi'an that oversees stormwater drainage performance in Hengshui, North China's Hebei Province Photos: Courtesy of Xi'an University of Technology
During Typhoon Muifa in 2022, Haishu district in Ningbo, East China's Zhejiang Province, recorded a regional rainfall of 367 millimeters. Using GAST as its core technology, the local flood forecasting platform integrated weather forecasts, AI algorithms and real-time monitoring data to provide rolling three-hour flood risk predictions.
Post-event assessments showed that predicted risks at most locations matched actual flooding conditions. The average relative error between predicted and observed maximum water depths was 13 percent.
The GAST model was also integrated into a smart rain and flood management platform in Qinhan new city area in Xianyang of Shaanxi, and during a rainstorm warning in July 2022, the platform provided continuous monitoring and forecasts. Based on the results, local authorities shifted from routine inspections to targeted monitoring of flood-prone areas and optimized emergency drainage operations.
The model is also being applied to mountain torrent prevention, as it can simulate rapidly changing flows in complex terrain and, combined with machine learning, complete forecasts within seconds. For reservoirs and rivers, it supports sudden and gradual dam-break simulations.
In June 2026, the model was presented at a national symposium on flood risk mapping achievements. The technology has since been applied by water resources, emergency management and urban development authorities, expanding from Shaanxi to multiple provinces and regions across China.
Looking ahead, the research team is developing a framework that further keeps up with the pace focusing on AI technologies. "Currently, the system operates based on weather forecast, therefore, AI will increase efficiency by using historical cases and real-time monitoring data to correct errors and update forecasts dynamically," Hou said.
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二 | 　　讯据水利部消息，8月19日上午，国家防总副总指挥、水利部部长李国英主持召开专题会商，视频连线水利部太湖流域管理局，分析研判太湖流域汛情发展态势，进一步安排部署太湖流域洪水防御工作。　　李国英指出，受今年第13号台风“白海豚”及其残余环流带来的强降雨影响，8月11日太湖发生2026年第1号洪水；8月14日水位涨至4.20米，太湖发生流域性较大洪水；经过科学精细调度，8月17日19时40分太湖出现本轮洪水最高水位4.49米（洪峰水位到4.50米时，太湖即发生流域性大洪水），成功避免发生流域性大洪水。当前，太湖水位总体呈波动缓退态势，预报将于8月底降至4.20米左右，9月上旬末退至警戒水位以下。太湖及周边河网长时间高水位运行，叠加风浪影响，堤防出险风险增加，加之后期可能有台风登陆影响，太湖流域防汛形势依然严峻复杂，不可掉以轻心。　　李国英要求，全力以赴做好六个方面重点工作。一要强化监测预报预警。充分运用雨水情监测预报体系和数字孪生太湖平台，对降雨、产流、汇流、演进进行全链条精准监测和滚动预报，动态推演分析太湖及周边河网地区水位及过程变化，为工程调度、巡查防守、人员转移等提供精准决策支持。二要尽快降低太湖及河网水位。调度太浦河、望虞河全力排水；调度新孟河界牌、奔牛枢纽全力外排湖西区洪水，减少湖西区进入太湖水量；督促指导地方充分挖掘工程潜力，沿江、沿海、沿杭州湾各水闸、泵站继续全力排水。三要加强堤防巡查防守。高度重视环太湖、望虞河、太浦河以及周边河网区超警、超保堤段，沿程逐段预测风险，提前划定风险区域；紧盯薄弱堤段、险工险段、穿堤建筑物等重点部位，严格落实巡堤查险措施；预置抢险力量、料物、设备，做到险情抢早、抢小、抢住，确保重要堤防不决口。四要科学实施上游水库调度。调度太湖上游青山、对河口等前期拦洪运用水库有序降低水库水位，在确保工程安全和下游安全行洪前提下，充分做好拦蓄后续台风暴雨洪水的各项准备。五要坚持流域“一盘棋”。