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Climate Gambit: Chinese team develops ‘super brain’ to guide flood precautions using weather, hydraulic and terrain data_我的网站
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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.
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.
。 AI摘要 大陆电影《给阿嬷的情书》首映台湾桃园电影节，票房秒罄、观众感动落泪，引发两岸共同历史记忆共鸣，但岛内放映受限令民众遗憾。 8月15日、16日，大陆电影《给阿嬷的情书》以“特别放映”的形式在台湾桃园电影节亮相。
B | 这是这部电影首次在台湾地区放映，不但电影票“一票难求”，十多分钟就告售罄，而且放映现场也早早排起长队。台湾观众对该片和大陆电影近年来的发展创新都给予了高度评价。 8月15日是电影《给阿嬷的情书》在台湾放映的第一天。首场电影在当天下午2点放映，台湾观众早早就在影厅门口排起了长队。这部电影在台湾一直有非常高的关注度，两场电影票刚开售十多分钟就告售罄。 台湾观众刘先生早就慕名去深圳看过影片，还专门去潮汕地区旅游“打卡”，买了不少纪念品。
C | 这次听说影片在台湾放映，特意带着“侨批”冰箱贴来“二刷”电影。 台湾观众 刘先生：很好看，很感人啊。
D | 我是去深圳，绕到潮汕，顺便去观光，买了一些冰箱贴，然后送给亲友了，只留这一个给自己。 台湾观众对《给阿嬷的情书》有天然的亲近感，电影中讲的华侨下南洋和历史上的“唐山过台湾”一样，都是闽粤沿海居民远离故乡、不畏艰险、跨海谋生创业的历史。
E | 影片放映后，不少观众红着眼眶走出影院，大呼感动。 台湾观众 郭女士：因为我阿公就是从潮阳走路到澄海，搭船去新加坡，他也是在那里拉三轮车。但他运气很好，他回来了，所以看到这部电影我眼泪就一直掉。 台湾观众 彭先生：我刚刚其实看得有共情到每一个角色的经历，写信、寄钱回家这些事情，我们以前完全不会知道。
F | 台湾观众 徐女士：电影演完了之后，通常来讲观众很自然就离场了。看完这场电影，我们居然全部都站在那边，那种心里的感动都还存在。 也有台湾观众高度评价近年来大陆影视作品的制作水准。有岛内人士也指出，对大陆电影当前在台湾受到的种种限制感到遗憾。 台湾观众 陈先生：近些年我觉得影视水准方面，不管是古装剧还是时装剧，我觉得都有非常高的质量，是让我们觉得耳目一新的。 台湾退休教师 区桂芝：这部电影除了用电影节的方式可以到台湾来，目前为止没有其他方式可以让大众看到，所以我其实觉得是有点遗憾的。
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