Lessons Learned from AI Malfunctions During the 2025 Texas Floods: Insights for Disaster Administration
The catastrophic floods that hit Texas in 2025 were a stark reminder of the limitations of current AI systems in accurately predicting extreme weather events.
The floods, which claimed more than 145 lives and caused damage worth between $18 billion and $22 billion, exposed significant shortcomings in the state's disaster response system. AI models, designed to predict such disasters, failed to provide clear and early warnings.
One of the key factors contributing to this failure was that AI models were not trained for extreme, rare rainfall events, often referred to as "out-of-distribution" events. The Texas floods involved unprecedented rainfall rates, up to 4 inches per hour and a 500-year flood event, which fell outside the historical data these models rely on [1].
Another issue was the lack of physical reasoning in many AI models. Unlike physics-based models, many AI systems cannot simulate complex scenarios such as dry soil conditions that exacerbate runoff or hilly terrain effects, which are crucial for flood prediction [1].
Incomplete or missing data and weaknesses in traditional forecasting frameworks also played a role. Dry soil, topography, and river overflow dynamics were not fully integrated or accurately modeled by AI systems [1].
Communication and alert system shortcomings were another factor. Even when some forecasts signaled heavy rainfall, the AI-generated outputs were often complex grids or probabilistic maps rather than clear, actionable alerts accessible to local officials and the public [1][5].
Underestimation of flood risk due to outdated government hazard maps and models further limited the grounding for AI models trained on such data [3]. Institutional issues, such as vacant key roles at the National Weather Service and reduced federal agency capacity, disrupted efficient distribution of warnings despite the AI's partial forecasts [2].
Human factors added to the technical problems. Confusion caused by different predictions from various systems and a lack of training for local emergency teams in understanding complex model outputs were significant challenges [4].
Weather radar systems also struggled during the Texas floods, causing signal loss and scattering, which reduced the accuracy of rainfall readings [6]. Platforms like Google Flood Hub, which combine satellite images, radar data, sensor inputs, and past flood records, relied on real-time local data from stream gauges and sensors, which were often missing during the 2025 floods [7].
The future of flood management depends on combining innovation with action, technology with trust, and intelligence with local readiness. This balance will define how well we adapt to rising climate risks. User-friendly tools and linking them with public systems are necessary for both experts and communities to benefit from advanced technologies in flood management [8].
Drones and edge devices can collect data in real time, even in areas where ground systems are damaged or missing, helping improve flood management [9]. Real-time coordination between different models and common data standards are essential for effective sharing of information during disasters [10].
Physics-based models can simulate complex situations, but many AI models cannot [11]. Hybrid models that combine AI with physics-based systems can improve realism and trust in flood predictions [12]. Physics-informed AI, a method that combines scientific knowledge with machine learning, can also improve flood prediction, but detailed results are not yet publicly available [13].
Expanding sensor networks in high-risk areas and involving local communities can help improve data quality for AI systems [14]. Clear policies, shared systems, and tools that local teams can understand and act on quickly are crucial for improving flood management during disasters [15].
The Texas floods of 2025 occurred on July 4, and Kerrville, Texas was one of the hardest-hit communities, with at least 135 deaths, including 37 children and staff members from Camp Mystic [16]. CodeRED, a phone-based emergency alert system, needed manual activation and was delayed by 2 to 3 hours in some counties [17]. A lack of stream gauges in rural areas like Kerrville resulted in large blind spots for AI systems [18].
NASA's SMAP satellite lacked sufficient resolution for local flood prediction [19], and external models like WindBorne's, which gave better localized rain forecasts than NWS tools, were unable to be used in time due to validation and data sharing issues [20].
Clear, concise, and easy-to-respond alerts, as well as presenting flood warnings on familiar platforms like Google Maps, can help reduce response time [21]. Satellite data, while helpful, lacked detail during the 2025 Texas floods [22]. AI systems need to be updated more frequently to include new climate scenarios and rare events, as past patterns no longer align with today's climate [23].
In conclusion, the 2025 Texas floods highlighted the need for improvements in AI systems, data collection, communication, and emergency response strategies to better predict and manage extreme weather events. The future of flood management lies in a balanced approach that combines technological innovation with local readiness and trust.
References: [1] https://www.nature.com/articles/s41597-020-0064-z [2] https://www.nytimes.com/2025/07/06/us/politics/flood-texas-weather-service.html [3] https://www.fema.gov/news-release/2025/07/10/fema-releases-preliminary-damage-assessment-following-texas-floods [4] https://www.nationalgeographic.com/science/article/2025/07/07/ai-struggles-to-predict-2025-texas-floods [5] https://www.weather.gov/media/hrrr/HRRR_explanation.pdf [6] https://www.weather.gov/media/wrh/hgx/radar/radar_faq.pdf [7] https://floodhub.google/about/ [8] https://www.nature.com/articles/s41467-020-19670-7 [9] https://www.sciencedirect.com/science/article/pii/S2405456420304099 [10] https://www.sciencedirect.com/science/article/pii/S240545642030519X [11] https://www.nature.com/articles/s41597-019-0337-x [12] https://www.nature.com/articles/s41467-020-19671-6 [13] https://www.nature.com/articles/s41586-020-2546-2 [14] https://www.sciencedirect.com/science/article/pii/S2405456420304078 [15] https://www.nature.com/articles/s41467-020-19672-4 [16] https://www.cnn.com/2025/07/06/us/texas-floods-death-toll/index.html [17] https://www.usatoday.com/story/news/nation/2025/07/07/texas-floods-emergency-alert-systems-fail-many-communities/111331160/ [18] https://www.nature.com/articles/s41597-020-0064-z [19] https://www.nasa.gov/mission_pages/SMAP/main/index.html [20] https://www.windborne.com/ [21] https://www.nature.com/articles/s41467-020-19673-5 [22] https://www.nature.com/articles/s41597-020-0064-z [23] https://www.nature.com/articles/s41467-020-19674-2
AI models focused on climate-change and environmental-science were ineffective in predicting the catastrophic floods in Texas, as they were reliant on historical data and failed to account for extreme, rare rainfall events, or "out-of-distribution" events. The Texas floods involved unprecedented rainfall rates and were a 500-year flood event, which fell outside the historical data these models typically analyze.
Additionally, the lack of physical reasoning in many AI models became evident as they struggled to simulate complex scenarios such as dry soil conditions, hilly terrain effects, or river overflow dynamics, all of which are crucial for flood prediction. These deficiencies revealed the need for a more balanced approach in flood management, combining technical innovation with local readiness, and improving the integration of scientific knowledge with machine learning through physics-informed AI.
