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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluating indoor thermal resilience of passive and low-power cooling shelters for outdoor workers in India

India’s 231 million outdoor workers are exposed to deadly heat during heat waves. We report results from a simulation-based study that focuses on passive design of cooling shelters for the hot and dry climate zone of India. The goal is to design a cooler-than-outdoor shelter for outdoor workers’ intermittent rest and recovery from heat stress during their arduous outdoor work, to avoid heat-related morbidities and mortality. Expected indoor thermal conditions in low-cost cooling shelters deploying a range of passive designs and low-power active measures are investigated, consistent with the current Indian standards and practices, located in the city of Jodhpur in western India. Heat resilience of the shelter was measured with the predicted reduction of hazardous hours inside the shelter based on the wet-bulb globe temperature (WBGT) and extended heat index. These measures include improving the building envelope, installing cool roofs, using internal thermal mass, and natural ventilation. Additionally, a low-power active measure of using ventilation fans and ceiling fans is explored. EnergyPlus-based simulation results show that simple, commonly available measures, particularly natural ventilation and ceiling fans, achieve the most significant reductions in overheating danger hours, by 22% and 21%, respectively, while more complex or costly passive strategies yield only marginal additional benefits. Moreover, combined measure packages are identified that can reduce the indoor WBGT by 8 to 10 °C, eliminating overheating danger hours. These findings highlight the critical importance of scalable, low-cost, and easily deployable cooling solutions for developing resilient cooling shelters for vulnerable outdoor workers during heat waves in India.

building performance↗
DOE OSTI2021

Urbanization-induced land and aerosol impacts on storm propagation and hail characteristics

Changes in land surface and aerosol characteristics from urbanization can affect dynamic and microphysical properties of severe storms, thus affecting hazardous weather events resulting from such storms such as hail and tornado. We examine the joint and individual effects of urban land and anthropogenic aerosols of Kansas City on a severe convective storm observed during the 2015 Plains Elevated Convection At Night (PECAN) field campaign, focusing on storm evolution, convective intensity, and hail characteristics. The simulations are carried out at the cloud-resolving scale (1 km) using a version of WRF-Chem in which the spectral-bin microphysics (SBM) is coupled with the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). It is found that the urban land effect of Kansas City initiated a much stronger convective cell and the storm got further intensified when interacting with stronger turbulence induced by the urban land. The urban land effect also changed the storm path by diverting the storm toward the city mainly resulting from enhanced urban land-induced convergence in the urban area and around the urban-rural boundaries. The joint effect of urban land and anthropogenic aerosols enhances occurrences of both severe hail and significant severe hail by ~ 20% by enhancing hail formation and growth from riming. Overall the urban land effect on convective intensity and hail is relatively larger than the anthropogenic aerosol effect, but the joint effect is much notable than either of the individual effects, emphasizing the importance to consider both effects in evaluating urbanization effects.

Lin, Yun↗
DOE OSTI2026

Gateway Cities Unplugged: (em)Powering Affordable Housing (Final Technical Report)

The Gateway Cities Unplugged: (em)Powering Affordable Housing (the “Project”) was launched to address a critical challenge in the U.S. building and energy sectors: enabling multifamily buildings especially those serving low and moderate‑income (LMI) communities to actively support grid modernization through Grid‑Interactive Efficient Buildings (GEB). Multifamily housing represents a large share of national building energy consumption, yet the sector faces persistent barriers to adopting advanced load flexibility technologies, including capital constraints, aging infrastructure, and limited access to demand‑side management tools. This Project sought to overcome those barriers by defining, evaluating, and designing commercially viable GEB technology packages for six representative multifamily properties across Massachusetts, Connecticut, and New York.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Strategies to mitigate urban heat: Effects on overheating and cloud formation

This study evaluates the effectiveness of various urban heat island (UHI) overheating mitigation strategies and their associated impacts on urban cloud dynamics and thermal processes. This study shows cloud-resolving and urban-resolving modeling results estimating the impact of Houston-Galveston heat mitigation scenarios and other resilient strategies contemplated in the Climate Adaptation Plan and Resilient Houston reports. The simulated scenarios include high intensity green rooftops, rooftop photovoltaic solar panels, enhanced urban irrigation, white/cool roofs and roads, and street trees. We contrast the adaptation scenarios against a present baseline case, a no city scenario and a larger and denser city as projected by the Houston-Galveston Area Council for 2045. During the daytime, cooling strategies such as cool roofs, cool roads, and green roofs exhibit superior performance in mitigating urban overheating. At night, enhanced urban irrigation emerges as the most effective cooling intervention. Cooling strategies significantly reduce sensible heat flux partitioning during the day, a process that reduces the uplift of air, suppressing the formation of urban shallow cumulus clouds. The extent of urban cloud mixing ratio is reduced in proportion to the decrease in sensible heating. Under the BEP-Tree scenario, which includes wind effects and evapotranspiration driven by a stomatal conductance model, urban trees demonstrated negligible environmental cooling effects and minimal urban cloud modifications. In contrast, the scenarios with more urban cooling and higher latent heat fluxes led to suppressed urban clouds. The net cooling effect achieved by the heat mitigation strategies is influenced by a combination of indirect processes, including the reduction of downwelling longwave radiation flux, due to reduced cloud presence, while some warming is attributed to a modest increase in shortwave radiation that offsets the cooling benefit. Additionally, reduced heat dissipation, weakened thermal gradients, and diminished vertical mixing over urban areas further moderate the cooling potential. These findings highlight the pivotal role of clouds and moist atmospheric processes in shaping the UHI effect and offer insights for designing more effective urban cooling strategies.

albedo changes↗
DOE OSTI2025

Evaluating Acoustic vs. AI-Based Satellite Leak Detection in Aging US Water Infrastructure: A Cost and Energy Savings Analysis

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $\$4$–$\$4.8$ billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes.

acoustic leak detection↗
DOE OSTI2026

A Comparative Analysis of Infrastructure-Based Perception Sensors for Intelligent Transportation Systems

The rise of privatized and public investment in smart city infrastructure and intelligent transportation systems has generated a heightened demand for perception sensors that effectively track and detect objects while being reliable in diverse weather and lighting conditions. This growing demand for perception sensors has accelerated their development and enhanced their capabilities. With these new capabilities, it is challenging to determine the most suitable sensing unit to use in each situation. Therefore, it is essential to have a comprehensive understanding of the benefits and limitations of each sensing unit to effectively leverage their capabilities. The purpose of this paper is to provide a detailed evaluation of various perception sensors. Additionally, this paper will demonstrate the benefits of combining multiple perception sensors, which complement each other by addressing data gaps inherent to single-sensor systems, to facilitate the creation of a digital twin that models the real world. The Infrastructure, Perception, and Control (IPC) team will conduct data analysis using data collected through field testing at traffic intersections in Colorado Springs, Colorado, to make comparisons between sensors. This research aims to provide clear and concise information about modern perception systems, which will support the development of intelligent transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

The Response of Summertime Organic Aerosol Composition to Emission Controls in the Northeastern United States

The responses of secondary organic aerosols (SOA) to emission controls remain uncertain due to the limited continuous long‐term measurements. Measurements of detailed carbonaceous aerosol composition in the New York City metropolitan region during the summers of 2001, 2009, 2011, and 2018 provide a look at the changes in summertime SOA over the past two decades when significant pollution reduction and emission controls occurred in the northeastern United States. Here, we show that the averaged nighttime SOA, especially the fresh local SOA, extreme mass concentrations resulted from anthropogenic volatile organic compounds reductions, while there is a little trend for the regional aged SOA. Meanwhile, these fresh SOA extremes occurred most often under conditions of high relative humidity during nighttime. The analysis presented here provides a comprehensive evaluation of the evolution of the increasingly important organic component of the submicron ambient aerosols in this major metropolitan area.

Zhang, Jie↗
DOE OSTI2026

Same Data, Different Audiences: Using Personas to Scope a Supercomputing Job Queue Visualization

Domain-specific visualizations sometimes focus on narrow, albeit important, tasks for one group of users. This focus limits the utility of a visualization to other groups working with the same data. While tasks elicited from other groups can present a design pitfall if not disambiguated, they also present a design opportunity—namely, the development of visualizations that support multiple groups. This development choice presents a trade-off of broadening the scope but limiting support for the more narrow tasks of any one group, which in some cases can enhance the overall utility of the visualization. We investigate this scenario through a design study where we develop Guidepost, a notebook-embedded visualization of data that helps scientists assess compute wait times, machine learning researchers understand prediction accuracy, and system maintainers analyze usage trends. We adapt the use of personas for visualization design from existing literature in the HCI and design domains, applying them to categorize tasks based on their uniqueness across stakeholder personas. Under this model, tasks shared between all groups should be supported by interactive visualizations and tasks unique to each group can be deferred to scripting with notebook-embedded visualization design. We evaluate our visualization through real-world case studies and a task-focused evaluation with nine participants. We observe that together, Guidepost's visual encodings, interactions, and export capabilities support the tasks of our differing personas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The role of building characteristics, demographics, and urban heat islands in shaping residential energy use

This study assesses the effects of residential building structure, socio-demographics, and ambient temperature conditions of a neighborhood to overall energy expenditures of a household. Using annual utility billing data, existing tax-lot records, temperature data, and U.S. Census demographic data, we ask two research questions: (1) What role do differences in neighborhood ambient heat, structural conditions and behavior play in helping to explain residential energy use? And (2) In what ways do energy expenditures spatially cluster in relation to urban heat islands? Using linear regression, spatial regression, and spatial clustering techniques, we evaluated the role of physical location of these households in relation to ambient temperatures. Significant positive relationships among energy expenditures and building size, average household size, White population, and population over 65 were observed; Significant negative relationships among energy expenditures and building density, renter population and population with a bachelor’s degree or higher were also observed. Negative relationships among morning and afternoon measures of heat, suggest that some areas spend less on energy use as a result of the neighborhood scale thermal environment. Spatial analysis of ambient temperature and energy expenditures show that energy use throughout the city is spatially clustered, with more affluent areas using more energy, regardless of whether heat islands are present. The results help to frame the role that increasing ambient temperatures in cities play in the spatial disparity in energy demand.

Residential Buildings, Energy Expenditure, Socio-d↗
DOE OSTI2023

Spatiotemporal Variability of Electric System Reliability Metrics

As urbanization continues to grow, urban density also rises, placing significant stress on critical infrastructure. Cities often face challenges in investing in new infrastructure or upgrading existing ones. One area of infrastructure that has come under strain is the electric grid, leading to prolonged power outages. According to data from the U.S. Energy Information Administration, the average duration of power outages has steadily doubled from 2013 to 2021, primarily due to extreme weather events. The IEEE has developed a series of reliability metrics to assess the reliability of the electric system. However, these metrics are only applied at the utility level, making it challenging to comprehend the variations in these indices at finer spatial resolutions and, consequently, evaluate reliability at the non-utility level. In this study, we leveraged electric outage data collected at 15-minute intervals at the county level to estimate electric system reliability over a six-year period using a novel data analysis approach. Our findings reveal a gradual decline in reliability in the U.S. Southwest compared to national averages. Furthermore, the duration of outages per customer per event has significantly decreased in large portions of California and Florida. These discoveries can be valuable for utility companies as they seek to pinpoint regional anomalies and plan for prioritized remediation efforts.

Singh, Nagendra↗
DOE OSTI2022

Online Calculator to Evaluate the Impact of Airtightness on Residential Building Energy Consumption and Moisture Transfer

Energy consumption in residential buildings is primarily driven by space conditioning applications. Space heating and cooling, on average, consume approximately 50% of the energy in the residential buildings in the U.S. The primary energy use due to infiltration is more than 2.8 Quads, which is 29% of primary energy consumption attributable to fenestration and building envelope components in residential buildings in US in 2010. There are advanced air barrier technologies and construction practices to reduce air leakage in buildings, which are currently available in the market. However, the lack of adequate information on their impact on energy consumption and the durability of buildings has caused the slow adoption of these technologies and methods. In the past, the authors developed an online calculator that estimates the potential energy and cost savings in major U.S., Canadian and Chinese cities from improvement in airtightness in commercial buildings. In 2018–2019, the calculator was expanded to add moisture transfer calculations, given that air leakage through the building envelope can have a significant impact on moisture transfer. The calculator is again being expanded by adding residential and additional commercial building data. In this paper, we present the impact of airtightness in residential buildings on energy consumption and moisture transfer. The study includes the analysis of airtightness in 52 major cities in the U.S. and five cities in Canada on a residential building that includes a crawlspace and has a gas furnace.

Kunwar, Niraj↗
DOE OSTI2025

Study of self-assembly behavior and ionic conductivity of conjugated liquid crystals with T-shaped facial-polyphilic structure

The unique self-assembly of liquid crystals (LCs), combined with their potential application as organic semiconductors, has become a focus of recent research. Here, a joint experimental and computational study of the self-assembly and ionic conduction was carried out on a series of T-shaped conjugated LCs consisting of three incompatible components. By extending the EOn side-chain length, several experimental evaluations confirmed a decrease of the order-disorder transition temperature, while coarse-grained simulations revealed a structural evolution from a smectic phase to a columnar phase. Ionic conductivity of these molecules was achieved by adding Li salt, leading to a maximum conductivity of 1.1 × 10 −3 siemens per centimeter observed at 120°C. All-atom simulations were performed to examine the Li-ion solvation environment and to evaluate the intrachain and interchain Li-ion hopping mechanisms. The molecule with a long EOn side chain was found to generate a densely distributed network of Li-ion solvation sites, which can facilitate effective interchain hopping to promote ion transport.

Liu, Ziwei [Cornell Univ., Ithaca, NY (United Stat↗
DOE OSTI2024

Mobility Energy Productivity Evaluation of On-Demand Transit: A Case Study in Arlington, Texas

On-demand transit (ODT) systems are increasing in number and size. To evaluate and quantify outcomes, the research team utilizes the mobility energy productivity (MEP) metric, which is a holistic accessibility measure, to analyze and compare the mobility of various transportation modes in Arlington, Texas. The MEP tool is applied to the ODT system in Arlington, Texas, as well as to five existing transportation modes (driving, transportation network company, transit, biking, and walking). Six ODT scenarios are also analyzed and compared. The analysis is focused on the opportunities that an ODT system presents for transportation disadvantaged communities (DACs) with low rates of car ownership. Although driving received the highest MEP score—a finding typical for a U.S.A. city— the results for the ODT system reveal that it serves those in DACs effectively, helping to achieve an equity design goal. ODT improved the average MEP score across the service area by 83% when considering only accessible, nonprivate vehicle modes (biking, transit, and ODT). For the ODT scenarios, decreasing the wait time by 50% compared with the baseline scenario led to a nearly 160% increase in the MEP score, whereas increasing the ODT travel speed by 21% led to an 80% improvement in the MEP score. As analyzed through the MEP tool, here this paper demonstrates how ODT can enhance mobility, particularly for DACs. The results of an MEP analysis can be used by researchers and transit agencies to compare transportation modes and improve the effectiveness of transportation systems across a service area.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Predicting the von Neumann entanglement entropy using a graph neural network

Calculating the von Neumann entanglement entropy from experimental data is challenging due to its dependence on the complete wavefunction, forcing reliance on approximations such as classical mutual information (MI). We propose a machine learning approach using a graph neural network to predict the von Neumann entropy directly from experimentally accessible bitstrings. We test this approach on a Rydberg ladder system and achieve a mean absolute error of $3.6\,\times 10^{-3}$ when evaluating within the training range on a dataset with entropy values ranging from 0 to 1.9. The model achieves a mean absolute percentage error of 1.44% and outperforms MI-based bounds. When tested beyond the training range, the model maintains reasonable accuracy. Furthermore, we demonstrate that fine-tuning the model with small datasets significantly improves performance on data outside the original training range.

graph neural networks↗
DOE OSTI2025

Southwest Regional Partnership on Carbon Sequestration: Phase III (Final Scientific/Technical Report)

The Southwest Regional Partnership on Carbon Sequestration (SWP) is one of 7 regional partnerships formed in 2003 under the U.S. Department of Energy’s (DOE) Regional Carbon Sequestration Partnerships (RCSPs) initiative. The overall purpose of the initiative was to help determine and implement the technology, infrastructure, and regulations most appropriate to promote carbon storage in different regions of the country. Covering Arizona, Colorado, New Mexico, Oklahoma, Utah, and parts of Texas, Wyoming, and Kansas, the SWP evaluated regional carbon storage and utilization potential and focused on technologies and sites that could complement the region’s strong position in energy production. The project progressed through three phases: • Phase I (2003–2005): Characterized regional geologic formations and CO 2 sources, assessed sequestration potential, and identified pilot test sites. • Phase II (2005–2013): Conducted small-scale field tests to validate sequestration methods, including geologic and terrestrial projects. • Phase III (2008–2022): Demonstrated large-scale CO 2 injection at a commercial oil field to test monitoring, verification, and long-term storage strategies. This report covers Phase III. The final project site, the Farnsworth Unit (FWU) in Texas, provided real-world testing of reservoir characterization, monitoring, and risk evaluation tools and processes that could be used in any commercial scale carbon capture, utilization, and storage (CCUS) project. Extensive data collection and analysis helped refine best practices for reservoir characterization, injection monitoring, and storage verification. The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Key lessons learned include the importance of robust data management, strategic site selection, regulatory navigation, and effective industry collaboration. The project’s findings will inform ongoing and future carbon storage initiatives. Task 1 (Regional Characterization) • The SWP continued to participate in national outreach efforts and NATCARB. • The SWP evaluated multiple potential sites before selecting the FWU as the primary field test location. Task 2 (Public Outreach and Education) • The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Task 3 (Permitting and Regulatory Compliance) • The SWP ensured compliance with federal and state regulations, including National Environmental Policy Act (NEPA) requirements. • The SWP obtained all necessary permits for drilling, injection, and monitoring activities. Task 4 (Site Characterization and Planning) • The SWP developed work plans for four key activities: characterization, simulation, monitoring and verification, and risk evaluation. • The SWP collected and synthesized legacy data from multiple sources to build initial static geological models and dynamic reservoir models demonstrating project feasibility. • The SWP conducted an initial risk evaluation and developed mitigation plans. Task 5 (Field Operations and Data Collection) • The SWP drilled, logged, and cored three characterization wells to gather critical subsurface data. • The SWP conducted multiple geophysical surveys, including 3D seismic, crosswell seismic, and vertical seismic profiling, to improve reservoir characterization. Task 6 (Monitoring and Verification) • The SWP performed extensive geological characterization using data from characterization wells and seismic surveys. • The SWP established a surface monitoring network to track CO 2 flux in soil gas, groundwater chemistry, and near-surface atmospheric CO 2 levels. • The SWP built and refined reservoir models to study the effects of relative permeability on simulation behavior and improve calibration with experimental data. Task 7 (Risk Assessment and Model Refinement) • The SWP conducted multiple studies to evaluate reservoir integrity, predict CO 2 plume behavior and improve predictive modeling capabilities. • The SWP refined geological models and used them to enhance the accuracy of simulation models. • The SWP continued quantitative risk assessment of top-ranked risks and strengthened the link between qualitative and quantitative risk methodologies.

02 PETROLEUM↗
DOE OSTI2025

Development of the United States GReenhouse Gas and Air Pollutants Emissions System (GRA 2 PES)

In the U.S., emissions of greenhouse gases and air pollutants are often developed independently. Here, we describe the GReenhouse gas And Air Pollutants Emissions System (GRA 2 PES), which provides gridded emissions of fossil-fuel carbon dioxide (ffCO 2 ) and 93 air quality (AQ) species for 17 combustion and non-combustion sectors at 4 km × 4 km spatial resolution across the contiguous US. We find that the AQ emissions most spatially correlated with ffCO 2 are nitrogen oxides (NO x , ρ = 0.67), followed by sulfur dioxide (SO 2 , ρ = 0.51), carbon monoxide (CO, ρ = 0.44), and fine particulate matter (PM 2.5 , ρ = 0.38). We evaluate GRA 2 PES ffCO 2 emissions with an ensemble of publicly available regional and global inventories at national (Normalized Mean Bias (NMB) = +1.4%), state (NMB = +1.5%, R 2 = 0.98), and urban (NMB = +11.5%, R 2 = 0.97) scales. Nationally, the differences of publicly available inventories from the ensemble average range from −10.0% to +5.7%, and consistency diverges at state and urban scales. We simulate GRA 2 PES ffCO 2 in a particle dispersion model and compare to measurements of radiocarbon ( 14 C)-derived ffCO 2 collected in Los Angeles (August 2021), with results suggesting that GRA 2 PES ffCO 2 may be low by 19% for this city, but well within model-observation differences for other publicly available inventories (−43% to +94%). GRA 2 PES AQ/ffCO 2 ratios converted to concentration space generally agree with field observations (NMB = +4%, log R 2 = 0.90). Lastly, we present a method by which to utilize GRA 2 PES to derive AQ emission fluxes from ffCO 2 emissions.

Lyu, Congmeng [National Oceanic and Atmospheric Ad↗
DOE OSTI2023

Data-driven modeling of municipal water system responses to hydroclimate extremes

Sustainable western US municipal water system (MWS) management depends on quantifying the impacts of supply and demand dynamics on system infrastructure reliability and vulnerability. Systems modeling can replicate the interactions but extensive parameterization, high complexity, and long development cycles present barriers to widespread adoption. To address these challenges, we develop the Machine Learning Water Systems Model (ML-WSM) – a novel application of data-driven modeling for MWS management. We apply the ML-WSM framework to the Salt Lake City, Utah water system, where we benchmark prediction performance on the seasonal response of reservoir levels, groundwater withdrawal, and imported water requests to climate anomalies at a daily resolution against an existing systems model. The ML-WSM accurately predicts the seasonal dynamics of all components; especially during supply-limiting conditions (KGE > 0.88, PBias < ±3%). Extreme wet conditions challenged model skill but the ML-WSM communicated the appropriate seasonal trends and relationships to component thresholds (e.g., reservoir dead pool). The model correctly classified nearly all instances of vulnerability (83%) and peak severity (100%), encouraging its use as a guidance tool that complements systems models for evaluating the influences of climate on MWS performance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

How extreme rainfall and failing dams unleashed the Derna flood disaster

On September 11, 2023, Storm Daniel unleashed unprecedented rainfall over the Wadi Derna watershed, triggering one of the most devastating floods in modern history, striking Derna, a coastal city in Libya. This study reconstructs the disaster using an integrated modeling approach that combines satellite imagery, hydrologic, hydraulic, and geotechnical simulations, machine learning, eyewitness accounts, and digital elevation data to assess the impact of cascading dam failures. Our findings reveal that the region’s dams, even if structurally sound, would have provided minimal protection against the extreme runoff. However, their failure unleashed a destructive surge wave, amplifying the disaster’s magnitude and devastation. Here, we show that the collapse of aging flood control infrastructures, compounded by inadequate risk assessment and emergency preparedness, dramatically escalated the disaster’s impact. Our findings underscore the urgent need for systematic dam safety evaluations, enhanced flood forecasting, and adaptive risk management strategies that address climate extremes and infrastructure vulnerabilities.

Hydrology↗