The devastating Hunga Tonga-Hunga Ha’apai underwater volcano erupted at ~04:15 UT on 15 January 2022. We captured the waves that erupted from the volcano propagating in the ionosphere by monitoring total electron content (TEC) perturbations utilizing ground-based global navigation satellite system (GNSS) receivers that receive electromagnetic signals transmitted from the geostationary satellites operated by the BeiDou Navigation Satellite System (BDS). Meanwhile, ground barometers detected unusual enhancements of air pressure traveling in the troposphere. A novel phenomenon shows that the waves can individually propagate with a speed of ~335 m/s in the ionosphere, which is faster than its’ ~305 m/s in the troposphere. We further examined multiple geophysical data at the particular site of the novel instrumental array. Analytical results show that the pressure enhancements traveling in the troposphere not only downward trigger ground vibrations mainly in the horizontal components without obvious time difference, but also upward, leading the secondary TEC perturbations with a ~12-min delay.

Electromagnetic signals transmitted from the Beidou geostationary satellites can be utilized to monitor changes in ionospheric total electron contents (TECs) at motionless ionospheric pierce points (IPPs) over the Earth’s surface 24 h a day. The TEC perturbations at close IPPs detected via distinct horizontal azimuths and elevation angles can be examined by utilizing different measuring geometries formed by the selected geostationary satellites and ground-based Global Navigation Satellite System (GNSS) stations. The M6.9 Menyuan earthquake occurred in northwest China on 7 January 2022. We collected TEC perturbations associated with the Menyuan earthquake at those motionless IPPs to examine the capability of the TEC measurements utilizing distinct horizontal azimuths and elevation angles. The experimental results show that the TEC perturbations associated with the earthquake traveled away from the area around the epicenter with velocities of ~800 m/s and ~1000 m/s in the ionosphere. The traveling TEC perturbations were consistently observed in different geometries. Such novel results show that the pronounced TEC perturbations can be obtained once the satellite hanging high over the Earth’s surface in front of the traveling TEC perturbations is selected. This study shows that geostationary satellites provide an excellent opportunity to conduct experiments on the advantage of the TEC observation technology.

Investigating various geophysical parameters from the Earth’s crust to the upper atmosphere is considered a promising approach for predicting earthquakes. Scientists have observed that changes in these parameters can occur days to months before earthquakes. Understanding and studying the impending abnormal phenomena that precede earthquakes is both urgent and challenging. On 5 September 2022, a magnitude 6.8 earthquake occurred in Sichuan, China, at 4:52:18 (Universal Time). The earthquake happened approximately 175 km away from an instrumental array established in 2021 for monitoring vibrations and perturbations in the lithosphere, atmosphere, and ionosphere (MVP-LAI). This array consisted of over 15 instruments that regularly monitor changes in various geophysical parameters from the subsurface up to an altitude of approximately 350 km in the ionosphere. Its purpose was to gain insights into the mechanisms behind the coupling of these different geospheres during natural hazards. The seven geophysical parameters from the MVP-LAI system simultaneously exhibited abnormal behaviors approximately 3 h before the Luding earthquake. These parameters include ground tilts, air pressure, radon concentration, atmospheric vertical electric field, geomagnetic field, wind field, and total electron content. The abnormal increase in radon concentration suggests that the chemical channel could be a promising mechanism for the coupling of geospheres. Furthermore, air pressure, the geomagnetic field, and total electron content exhibited abnormal characteristics with similar frequencies. Horizontal wind experienced temporary cessation or weakening, while vertical wind displayed frequent reversals. These anomalies may be attributed to atmospheric resonance before the earthquake. The results demonstrate that the coupling of geospheres, as indicated by the anomalous phenomena preceding an earthquake, could be influenced by multiple potential mechanisms. The multiple anomalies observed in this study provided approximately 3 h of warning for people to prepare for the seismic event and mitigate hazards.

The integration of large language models (LLMs) into project-based learning (PBL) holds significant potential for addressing enduring pedagogical challenges in engineering education, such as providing scalable, personalized support during complex problem-solving. Grounded in Self-Determination Theory (SDT), this study investigates how different LLM usage strategies impact student learning within a blended engineering geology PBL context. A one-semester quasi-experiment (N = 120) employed a 2 (usage mode: individual/shared) × 2 (interaction restriction: restricted/unrestricted) factorial design. Mixed-methods data, including surveys, interaction logs, and reflective reports, were analyzed to assess learning engagement, psychological needs satisfaction, cognitive interaction levels, and project outcomes. Results demonstrate that the individual use strategy significantly outperformed shared use in enhancing engagement, needs satisfaction, higher-order cognitive interactions, and final project scores. The restricted interaction strategy effectively served as a metacognitive scaffold, optimizing the learning process by promoting deliberate planning. Notably, individual autonomy did not undermine collaboration but enhanced it by improving the quality of individual contributions to group work. Students also developed robust critical verification habits to navigate LLM “hallucinations.” This research identifies “individual autonomy” as the core mechanism and “moderate constraint” as a crucial design principle for LLM integration, providing an empirically supported framework for harnessing generative AI to foster both motivational and cognitive outcomes in engineering PBL.

This research is part of the ongoing research project − Climate change Adaptation to ManagE the risks of extreme hydrologicaL and weather events for food security in vulnerable west Nile delta (CAMEL). The study area, West Nile Delta, is an important region in Egypt featuring agricultural and industrial significance to the nation, whilst it faces serious crises from the interaction of complex environmental problems (e.g. flooding) which is exacerbated by climate change in the recent decades. Under the pressure of growing population, food security has become a national issue. In the latest decades, the region has experienced more extreme weather events; the severe rainfall events have resulted in flooding destroying massive crops and causing losses of human life and livestock. The evolvement of society in this region has made the people living in the flood-prone area – mostly farm labours − relatively socio-economic vulnerable. This research hence focuses on the urgent foregoing issue − disastrous pluvial flooding, which seeks to mitigate the issue of crop production loss and human casualty caused by climate change. Therefore, an adaption measure of an early warning system for extreme events caused by heavy rainfall has become an urgent demand. However, the scarcity of data (e.g. insufficiency in the coverage of gauge stations and radar stations) has always been a main obstacle to relevant measures in Egypt. The research hence seeks to cope with such difficulty whilst to build an integrated flood early warning system for Egypt. Based on the integration of Nowcasting method (applying GPM and MPE satellite radar observation) and NWP method (downscaling ECMWF data) as the substitution for the insufficient ground observations, the integrated approach can take the advantages of both data sources to perform better forecasting. However, GPM and MPE data, compared with ground observation data, still reflects relative disadvantages in spatial and temporal resolution in terms of Nowcasting application. Besides, notwithstanding Nowcasting method can make up for the spatial resolution of the NWP method, its mainstream − optical flow approach based on the Lagrangian method − still lacks confidence in dealing with local advection circumstance, as well as fast and drastic formation and dissipation of precipitation. The research hence seeks to improve Nowcasting, by applying a phase-based frame interpolation method based on the Eulerian method, to refine the resolution of data to improve the performance of Nowcasting. It features better performance in precipitation change, strong precipitation divergence (i.e. light contrast), and computational efficiency. The improved Nowcasting, for further integrating with the NWP method, is being tested and proposed, which will end up with a recommendation of policy and a novel tool – real-time flood early warning system – so as to accommodate the hydrological extremes towards climate change in Egypt.