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Artificial intelligence will be deployed increasingly in more systems that affect public health, safety, and welfare. These systems will better utilize scarce resources; prevent disasters; and increase safety, reliability, comfort, and convenience. Despite the technological challenges and public fears, these systems will improve the quality of life of millions of people worldwide.
Abstract Negative extreme anomalies in vegetation growth (NEGs) usually indicate severely impaired ecosystem services. These NEGs can result from diverse natural and anthropogenic causes, especially climate extremes (CEs). However, the relationship between NEGs and many types of CEs remains largely unknown at regional and global scales. Here, with satellite‐derived vegetation index data and supporting tree‐ring chronologies, we identify periods of NEGs from 1981 to 2015 across the global land surface. We find 70% of these NEGs are attributable to five types of CEs and their combinations, with compound CEs generally more detrimental than individual ones. More importantly, we find that dominant CEs for NEGs vary by biome and region. Specifically, cold and/or wet extremes dominate NEGs in temperate mountains and high latitudes, whereas soil drought and related compound extremes are primarily responsible for NEGs in wet tropical, arid and semi‐arid regions. Key characteristics (e.g., the frequency, intensity and duration of CEs, and the vulnerability of vegetation) that determine the dominance of CEs are also region‐ and biome‐dependent. For example, in the wet tropics, dominant individual CEs have both higher intensity and longer duration than non‐dominant ones. However, in the dry tropics and some temperate regions, a longer CE duration is more important than higher intensity. Our work provides the first global accounting of the attribution of NEGs to diverse climatic extremes. Our analysis has important implications for developing climate‐specific disaster prevention and mitigation plans among different regions of the globe in a changing climate.
Effective flood simulation capabilities can tremendously support early warning and disaster prevention. To examine the applicability of a fully physics-based and high-performance flood simulation and forecasting modeling framework for a flood-prone region in Taiwan, we conduct a numerical experiment that couples the Weather Research and Forecasting (WRF) Model, WRF-Hydrological modeling system (WRF-Hydro), and the Two-Dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) to perform integrated rainfall, streamflow, and flood simulations. Furthermore, we first use the coupled WRF and WRF-Hydro (WWH) to predict rainfall and streamflow and then drive TRITON with the predicted streamflow hydrographs to simulate flood depth and inundation area. With the refined spatial resolution and parameterization, this framework can better predict rainfall with reasonable spatial patterns. Although WWH could overestimate the amount of rainfall in some areas, the uncertain rainfall–streamflow predictions produce reasonable flood maps able to pinpoint regions at risk of flooding. In terms of model efficiency, the graphics processing unit–based computation can yield a speed-up factor as high as ∼13 compared to the central processing unit–based computation, promoting the efficacy of the coupled modeling framework in practical real-time flood forecasting.
Accurate short-term precipitation forecast is extremely important for urban flood warning and natural disaster prevention. In this paper, we present an innovative deep learning model named ISA-PredRNN (improved self-attention PredRNN) for precipitation nowcasting based on radar echoes on the basis of the advanced PredRNN-V2. We introduce the self-attention mechanism and the long-term memory state into the model and design a new set of gating mechanisms. To better capture different intensities of precipitation, the loss function with weights was designed. We further train the model using a combination of reverse scheduled sampling and scheduled sampling to learn the long-term dynamics from the radar echo sequences. Experimental results show that the new model (ISA-PredRNN) can effectively extract the spatiotemporal features of radar echo maps and obtain radar echo prediction results with a small gap from the ground truths. From the comparison with the other six models, the new ISA-PredRNN model has the most accurate prediction results with a critical success index (CSI) of 0.7001, 0.5812 and 0.3052 under the radar echo thresholds of 10 dBZ, 20 dBZ and 30 dBZ, respectively.
Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.
Natural gas hydrate production tests face problems such as severe sand blockage, poor gas-liquid phase separation, and significant land subsidence. This is because of the insufficient understanding of the complex phase transition and gas-liquid multi-phase fluid migration during hydrate dissociation. In hydrate-bearing sediment systems, hydrate phase transition couples with gas-liquid fluid migration. The phase transition causes changes in pore structure, which in turn modifies porous infiltration parameters and fluid flow capacity. Meanwhile, alterations in phase interfaces affect key parameters like surface tension and wettability. Gas-liquid fluid migration influences heat and mass transfer, thus affecting phase equilibrium and dissociation rates. To bridge the gap in describing gas-liquid fluid migration during hydrate dissociation in experiments, this research innovatively integrated an unsteady-state gas displacement by water and a quantitative hydrate dissociation process, independently developed a multi-phase seepage experimental system suitable for hydrate dissociation and determined the relationship between seepage parameters and hydrate saturation under different porosity. The results are as follows: a) Core samples with higher initial porosity show a greater recovery rate of fluid flow capacity. b) The retarding effect of multi-phase fluid has a more significant impact on the migration of the wetting phase fluid (water) than that of the non-wetting phase fluid (methane). c) During hydrate dissociation, the evolution of absolute permeability shows an “S-shaped” pattern, and the evolution of relative permeability shows a “wiring-harness” pattern. In conclusion, the findings can provide a theoretical basis for preventing geological disasters and for geotechnical engineering design during hydrate production.
Earthquake strong-motion predictions using kinematic source modeling require knowledge of the slip-rate functions (SRFs) along the rupture and their distinct characteristics in asperities, background (off-asperity) areas and near the surface. Here, in this study we analyzed SRFs from well-validated, self-consistent, and fully dynamic rupture models from earthquake cycles obeying a rate-and-state friction law, from our companion study (Galvez et al., 2021). The shapes of SRFs in asperities are well described by the regularized Yoffe function (RYF), which has only two parameters: rise time T r and smoothing time T s , which control the generation of long- and short-period ground motions, respectively. In background areas, we demonstrate that, in addition to the primary rupture, multiple secondary ruptures may also nucleate from rupture heterogeneities related to asperities, resulting in SRFs with multiple peaks. Because it is impossible to fit a multiple-peak SRF by the single-peak RYF, we describe SRFs in background areas in an effective way by fitting their amplitude spectra with the RYF spectra. Such spectrally effective RYFs capture salient aspects of seismic-wave generation and can be used in rupture generators for strong motion prediction. We found that small T s values correlate with small characteristic weakening distances, large peak slip rates (PSRs), and large rupture velocities. T r values are larger in background areas and smaller in asperities. Within the shallow aseismic zone, T s values approximately quadruple whereas T r values approximately double. Because of this dominant T s increase, PSR values decrease in the near-surface zone. These features indicate that the generation of strong motions by the near-surface portions of the rupture is negligible in the studied scenarios.
A plethora of information exists in literature focusing on different aspects of personal resilience. There are a number of questionnaires available to measure resilience in different aspects of an individual’s life, such as mental health. However, there is a lack of comprehensive information on the subject of personal resilience to natural and man-made disasters. A Personal Resiliency Index identifying four key areas of resilience related to natural and man-made disasters will help fill this gap by providing individuals an idea of where the stand comparatively to their peers on this topics. As an increasing amount of man-made disasters, natural disasters and pandemics occur, we realize that preparation is key.?“Crisis can be experienced at individual, group and mass levels and at local, regional, national or supra-national levels.” 1? Dr. Ron Fisher, the principal investigator has over 20 years’ critical infrastructure protection including developing vulnerability assessment methodology, risk and resiliency analyses and infrastructure interdependencies. Dr. Fisher and his team will research and develop a voluntary survey which evaluates the user’s personal resilience level to natural and man-made disasters. The research team is focusing on 4 main categories:? financial resilience, cyber security, physical and mental health, and emergency preparedness.?The web based application will?provide a resilience score for each of the categories and also a comprehensive personal resiliency score based on the voluntary survey. The application will also provide feedback to the user on actions to take to become more resilient to natural and man-made disasters. Through this effort, we will also be able to identify the areas where participants are excelling as well as opportunities for improvement in terms of resilience. This information will provide insight into areas of further investigation and identify potential areas of support needed for the general public to adopt strategies to become more resilient. 1 Lynott, William. “Surviving a Natural (or Man-Made) Disaster: How to Prepare for and Bounce Back? from a Crisis in Your Business | 2018-02-01 | NOLN.” Home Page, NOLN, 1 Feb. 2018, https://www.noln.net/articles/2683-surviving-a-natural-or-man-made-disaster-how-to-prepare-for-and-bounce-back-from-a-crisis-in-your-business.
The Essential Function Analysis Capability (EFAC) is an extension of the All Hazards Knowledge Framework (AHA) and is a dynamic analytical framework that enables critical infrastructure knowledge discovery and decision support across the five mission areas – prevention, protection, mitigation, response and recovery. EFAC provides the ability to store and model infrastructure systems as a linked multigraphs providing an intuitive and natural representation. These infrastructure systems can then be tied to the organizational and essential function breakdown of an entity. This capability provides the foundation to rapidly evaluate and understand the potential consequences of manmade and natural disaster on these infrastructure systems and essential functions.
This research introduces a more efficient way to optimize power grid operations, breaking the problem into manageable steps and using advanced mathematical techniques to speed up calculations. By incorporating smart heuristics, improved preprocessing, and contingency analysis, the approach allows operators to make better decisions faster. These innovations enhance our understanding of how to optimize energy generation, making it possible to anticipate failures before they happen, reduce system costs, and improve overall grid performance. Ultimately, this research helps bridge the gap between theoretical models and real-world applications, paving the way for a smarter, more resilient power grid. This research directly benefits the public by making electricity more affordable, reliable, and sustainable. By improving how power grids schedule and distribute electricity, the project helps energy providers reduce operational costs, which can lead to lower electricity prices for consumers. Additionally, the ability to predict and prevent power system failures enhances grid reliability, reducing the likelihood of blackouts that can disrupt homes, businesses, and critical infrastructure such as hospitals. From an environmental perspective, optimizing power generation reduces energy waste and lowers carbon emissions, contributing to cleaner air and a more sustainable energy system. Furthermore, with extreme weather events becoming more frequent, these advancements make the power grid more resilient, ensuring communities are better prepared for emergencies and natural disasters. By strengthening the nation's energy infrastructure, this research plays a crucial role in improving economic stability, public safety, and environmental sustainability.
Addressing the current health of the nation’s existing 3 million miles of pipeline infrastructure is key to preventing further climate change. In 2020, natural gas production exceeded 34 trillion cubic feet (Tcf). Roughly 75% of natural gas consists of methane (CH 4 ), which is up to 25 times more powerful than carbon dioxide (CO 2 ) at trapping heat within the atmosphere over a 100-year period, and studies from the Environmental Defense Fund (EDF) estimate approximately 2% of all the natural gas produced will be lost during normal operations due to unaddressed leaks. This does not even consider the risks of major disaster due to pipeline failure, or the losses and extra fuel costs incurred due to corrosion and scale deposits in under-maintained pipelines. The objective of the proposed research is to demonstrate the protection capabilities and economic benefits of Oceanit’s internal pipe surface treatment, known as DragX™. DragX™ is a chemically resistant, water-and-oil repellent nanocomposite system that can be readily applied in-situ on natural gas transmission and distribution pipelines with a minimum of surface preparation. This makes it an ideal candidate for in-place retrofitting and refurbishment of existing pipelines without the need for expensive extraction and replacement. DragX™ is also able to significantly reduce the surface roughness, and subsequently, the frictional drag forces within a pipeline, improving throughput, decreasing energy costs of pressurization and pumping, and allowing for longer pipeline operation without interruption, reducing the methane emitted during pipe isolation and venting. As part of this project, Oceanit has utilized the Department of Energy’s support to fully develop, de-risk and prove the DragX™ core technology is both economically viable and commercially desirable to pipeline operators and energy companies alike. DragX™ material properties were optimized in this effort both for ease of applicability, to provide value in certain key parameters, and was demonstrated on pilot applications exceeding 2 miles in length. Beyond the already field demonstrated applications, this innovative nanocomposite surface treatment has the potential to be the backbone for CO 2 and Hydrogen transporting pipeline infrastructure. The learnings from this project could accelerate the deployment of surface treatment technologies related to the energy transition infrastructure, thus benefitting the clean energy initiatives in the United States and all around the world.
Industrial accidents, such as the Fukushima and Chernobyl disasters, release harmful chemicals into the environment, covering large geographical areas. Natural flora may serve as biological sensors for detecting metal contamination, such as cesium. Spectral detection of plant stresses typically employs a few select wavelengths and often cannot distinguish between different stress phenotypes. In this study, we apply hyperspectral reflectance imaging in the visible and near-infrared along with multivariate curve resolution (MCR) analysis to identify unique spectral signatures of three stresses in Arabidopsis thaliana : salt, copper, and cesium. While all stress conditions result in common stress physiology, hyperspectral reflectance imaging and MCR analysis produced unique spectral signatures that enabled classification of each stress. As the level of potassium was previously shown to affect cesium stress in plants, the response of A. thaliana to cesium stress under variable levels of potassium was also investigated. Increased levels of potassium reduced the spectral response of A. thaliana to cesium and prevented changes to chloroplast cellular organization. While metal stress mechanisms may vary under different environmental conditions, this study demonstrates that hyperspectral reflectance imaging with MCR analysis can distinguish metal stress phenotypes, providing the potential to detect metal contamination across large geographical areas.
Removal of radioactive Cs from sodium-rich solutions is a technical challenge that goes back to post World War II nuclear waste storage and treatment; and interest in this topic was reinvigorated by the Fukushima-Daiichi nuclear power plant disaster, 10 years ago. Since the 1960's there has been considerable focus on layered Zr phosphates as robust inorganic sorbents for separation of radionuclides such as Cs. Here we present synthesis and characterization, and direct comparison of Cs sorption capacity and selectivity of four related materials: 1) crystalline α-Zr phosphate and α-Hf phosphate, and 2) amorphous analogues of these. Powder X-ray diffraction, thermogravimetry, solid-state 31P magic angle spinning nuclear magnetic resonance (MAS-NMR) spectroscopy, and compositional analysis (inductively coupled plasma optical emission spectroscopy and mass spectroscopy, ICP OES and ICP MS) provided formulae; respectively M(HPO4)2∙1H2O and M(HPO4)2∙4H2O (M = Hf, Zr) for crystalline and amorphous analogues. Maximum Cs loading, competitive Cs-Na selectivity and maximum Cs-Na loading followed by the above characterizations plus 133Cs MAS-NMR spectroscopy revealed that amorphous analogues are considerably better Cs-sorbents (based on maximum Cs-loading and selectivity over Na) than the well-studied crystalline Zr-analogue. Additionally, crystalline α-Hf phosphate is better Cs-sorbent than crystalline α-Zr phosphate. All these studies consistently show that Hf phosphate is less crystallize than Zr phosphate, when obtained under similar or identical synthesis conditions. We attribute this to lower solubility of Hf phosphate compared to Zr phosphate, preventing ‘defect healing’ during the synthesis process.