Rare earth elements (REEs) are a type of frequently reported emerging pollutant that affects plant growth. The harm caused by continuous exposure to low-dose REEs has rarely been studied. Quickly, accurately, and noninvasively monitoring the continuous influence of low-dose REEs on plant growth in situ is key to indicating and warning of its harm to plants and ecosystems. In this study, after continuous exposure to low-dose lanthanum [La(III), a REE] for 14 days, invisible damage occurred in leaf cells, and La accumulated continuously in the soybean plants (leaves > stems > roots > pods > seeds), causing potential human health risks. Two proteins [vitronectin-like protein (VN) and arabinogalactan proteins (AGP)] in leaf cells that bound La(III) were selected as biomarkers, and changes in these two proteins were detected by constructing dual-sensors in living leaf cells after continuous exposure to low-dose La(III) for 14 days. The results showed that the electrochemical outputs from leaf cells-the electron transfer resistance Ret(VN) and Ret(AGP)-were related to the damage indices such as MDA, chlorophyll content, electrolyte leakage, cell vitality, fresh and dry weight of leaves, and leaf area. Using this output, two warning intervals of visible damage were obtained: Ret(VN) was 8.53 %-47.22 %, and Ret(AGP) was 12.75 %-51.31 %. This study successfully demonstrated the real-time in situ detection of plant cell biomarker changes and invisible damage under low-dose La(III) exposure, providing methods for early warning monitoring of plant damage caused by low-dose continuous exposure to REEs.

To enhance the safety and reliability of urban buried water supply networks, this study developed a monitoring and early warning system based on wireless transmission networks and Internet of Things (IoT) technology. Through numerical simulations, the structural tilt warning thresholds for ductile iron pipes were determined. Additionally, in conjunction with meteorological data, monitoring pore water pressure serves as a supplementary indicator for detecting potential pipeline leakage. This study further analyzed pipeline strength warning thresholds based on strength theory. In practical engineering applications, the proposed system enables real-time monitoring of the operational status, service environment, and structural integrity of buried water supply networks. Data analysis revealed the influence mechanisms of backfill soil conditions, daily operations, and third-party construction activities on the structural behavior and stress state of water supply pipelines. Results indicate that during the initial backfilling phase, uneven backfilling and soil settlement induce significant variations in pipeline tilt angle and stress distribution. Furthermore, longitudinal stress in the pipeline exhibits a strong correlation with ambient temperature fluctuations, with a pronounced increase observed during colder months. Notably, third-party construction activities were identified as a major contributor to pipeline anomalies, with all recorded early warnings in this study being attributed to such external interferences.

On the morning of July 30, 2024, a catastrophic landslide struck Wayanad, India, in the ecologically sensitive Western Ghats, claiming over 260 lives, with many still missing beneath the debris. Here, we present a comprehensive overview of the landslide event based on field, satellite, and aerial images analysis, numerical modeling, and geotechnical testing to unravel the failure mechanism and its catastrophic impact on downstream communities. Our analysis revealed that a pre-existing crack, formed in 2020, acted as the initiation point for the recent failure. The underlying weathered and sheared geology, coupled with structural discontinuities, and thick soil strata, exacerbated by intense rainfall on July 29-30, catalyzed the transition of a planar slide into a catastrophic debris flow. Numerical simulations indicate that the debris flow initiated around 01:00 h, peaked at 04:00 h, and reached a maximum velocity of 28 m/s. The estimated volume of displaced material ranged between 5.17 x 106 and 5.72 x 106 m3, ranking it among the largest debris flows in India. The flow's run-up height in the transitional zone reached 32 m, amplified by multiple damming effects and topographic features such as cascades and river sinuosity, causing extensive infrastructure damage to the downstream population. Given the terrain's known fragility and history of sequential events, this region requires urgent attention for real-time monitoring and mitigation strategies to reduce future risks.

Rising infrastructure density and transportation networks along the riverbank landslide alter critical stress and horizontal displacement in riverbank soils, contributing to erosion. Early warning systems can detect structural changes in soil to help mitigate damage. However, there is still a lack of studies evaluating horizontal pressure in landslide masses under the influence of load and horizontal displacement causing erosion or externally induced stress. This study presents a monitoring system based on wireless transmission technology combined with sensors embedded in the soil to track the displacement of the soil mass along the riverbank. The system uses tilt, soil moisture, and earth pressure sensors to collect real-time data on the mechanical properties of the soil. Experimental results show that a load of 17.5 kPa can destabilize the slope, with tilt angles increasing significantly as soil mass shifts toward the canal. The maximum recorded horizontal soil pressure is 2.77 kPa. The analysis reveals significant discrepancies between analytical methods and finite element method (FEM) in predicting soil behavior under loads, highlighting the superior accuracy of FEM, especially at higher loads. This research contributes to developing a reliable information system for managing landslide risks as well as externally induced stress, protecting people and infrastructure.

Landslides present a significant global hazard, resulting in substantial socioeconomic losses and casualties each year. Traditional monitoring approaches, such as geodetic, geotechnical, and geophysical methods, have limitations in providing early warning capabilities due to their inability to detect precursory subsurface deformations. In contrast, the acoustic emission (AE) technique emerges as a promising alternative, capable of capturing the elastic wave signals generated by stress-induced deformation and micro-damage within soil and rock masses during the early stages of slope instability. This paper provides a comprehensive review of the fundamental principles, instrumentation, and field applications of the AE method for landslide monitoring and early warning. Comparative analyses demonstrate that AE outperforms conventional techniques, with laboratory studies establishing clear linear relationships between cumulative AE event rates and slope displacement velocities. These relationships have enabled the classification of stability conditions into essentially stable, marginally stable, unstable, and rapidly deforming categories with high accuracy. Field implementations using embedded waveguides have successfully monitored active landslides, with AE event rates linearly correlating with real-time displacement measurements. Furthermore, the integration of AE with other techniques, such as synthetic aperture radar (SAR) and pore pressure monitoring, has enhanced the comprehensive characterization of subsurface failure mechanisms. Despite the challenges posed by high attenuation in geological materials, ongoing advancements in sensor technologies, data acquisition systems, and signal processing techniques are addressing these limitations, paving the way for the widespread adoption of AE-based early warning systems. This review highlights the significant potential of the AE technique in revolutionizing landslide monitoring and forecasting capabilities to mitigate the devastating impacts of these natural disasters.

The Himalayan foothills are highly prone to rainfall induced flash floods. This research focuses on the August 19-20, 2022 flash flood event in Song watershed of Doon valley, Uttarakhand caused significant damages to buildings and a road bridge. The study aims to assess the flood intensity through flood simulation in a semi-distributed hydrological model by utilizing rainfall data, land use and soil data. Further, the flood hydrographs generated through hydrological modelling were used to simulate hydrodynamic model to estimate flood depth. Pre and post-flood inundation assessments were conducted using PlanetScope and Sentinel-1 imagery. Furthermore, development activities on river courses were analyzed utilizing Google earth and Bing maps high resolution imagery. Cumulative rainfall observations revealed 344 mm rainfall in Rishikesh and 225 mm in Sahastradhara on 19-20 August for the 24 hrs, contributed in a peak flood discharge 2679 m(3)/s at the Rishikesh outlet. The simulated flood depth depicted 4.81 m flood depth at the damaged Thano-Bhogpur bridge. The PlanetScope satellite imagery showed 182 m expansion in the cross-sectional width of river at Maldevta after the flood. A 5.36 sq. km. flood area observed throughout the entire Song catchment in two days post event Sentinel-1 imagery. Analysis of high-resolution imageries revealed increasing development activities in floodplains of the catchment, which got affected by flood. The findings indicate urgent need of floodplain management by implementing comprehensive flood risk management plans including early warning systems, land-use regulations based on flood hazard zonation and flood resilient infrastructure to mitigate future flood exposure to society.

Slope failures are an ongoing global threat leading to significant numbers of fatalities and infrastructure damage. Landslide impact on communities can be reduced using efficient early warning systems to plan mitigation measures and protect elements at risk. This manuscript presents an innovative geophysical approach to monitoring landslide dynamics, which combines electrical resistivity tomography (ERT) and low-frequency distributed acoustic sensing (DAS), and was deployed on a slope representative of many landslides in clay rich lowland slopes. ERT is used to create detailed, dynamic moisture maps that highlight zones of moisture accumulation leading to slope instability. The link between ERT derived soil moisture and the subsequent initiation of slope deformation is confirmed by low-frequency DAS measurements, which were collocated with the ERT measurements and provide changes in strain at unprecedented spatiotemporal resolution. Auxiliary hydrological and slope displacement data support the geophysical interpretation. By revealing critical zones prone to failure, this combined ERT and DAS monitoring approach sheds new light on landslide mechanisms. This study demonstrates the advantage of including subsurface geophysical monitoring techniques to improve landslide early warning approaches, and highlights the importance of relying on observations from different sources to build effective landslide risk management strategies.

Tropical cyclone is a natural disaster phenomenon that is considered one of the main challenges for human populations in many countries across the globe. This study investigates the Tropical Cyclone Shaheen that hit Oman on October 3, 2021, causing severe damage near Muscat and Al-Batinah governorates. Herein, we focused on developing an integrative method using remote sensing and GIS to understand the streams' physical characteristics and the main factors that influence flood damage. The results showed that the cyclone had severe impacts on the study area, especially on the urban and agricultural areas. It was illustrated that the disturbance level differed within the study site, as the highest disturbance occurred in zone C where vegetation coverage decreased by 27% and urban areas by 5%. This zone had dam but still showed the highest amount of water flooding, illustrating that the dam couldn't prevent the flood due to the differences in the physical characteristics of the streams between the different zones. It was also illustrated from the results that variation in the degree of damage was associated with the physical characteristics of the streams, including the length of the stream, the number of stream sub-orders, stream depth, slope degree, and the soil type. Also, locations dominated by loamy and clayey soils with high, steep slopes had a great influence on the water movement, leading to a higher level of disturbance. We concluded that the discovered lines of evidence on the stream's physical characteristics in this study, including the combination of the examined stream's physical factors, could help predict the level of future cyclone risks.

The development of real-time early warning systems is crucial for mitigating landslide risks. Although internet coverage is extensive in urban areas, it often fails to reach remote locations such as mountainous regions. The low-power wide area (LPWA) communication network offers a viable alternative for transmitting data from landslide early warning system (LEWS) sensors to a central server. To develop an accurate and reliable LEWS, it is essential to establish appropriate thresholds for warning triggers. This study conducted a series of laboratory experiments on slope models, both with and without vertical cracks. The models were subjected to varying rainfall intensities to investigate the mechanisms of slope failure. The objective of this paper was to evaluate a cost-effective and sustainable LEWS based on internet of things using the Internet (WiFi) and LPWA for data transmission, and to monitor slope vulnerability. During the experiments, volumetric water content, pore water pressure, and tilt angle were measured. Thresholds for critical volumetric water content, pore water pressure rate, and tilt rate were proposed to define warning stages. The results contribute to enhancing the advancement of early warning systems, which are crucial for mitigating the risks associated with landslides.

Economic and human losses from flooding have had a significant global impact. Undeveloped nations often require extended periods to recover from flood-related damage, exacerbating the climate poverty trap, specifically in flood-prone regions. To address this issue, early warning systems (EWS) provide lead time for preparedness and measures to reduce vulnerability. However, EWS are mainly empirical at large scales and often do not incorporate hydrodynamic behaviors in flood forecasting, at least in developing regions with a lack of information. This study presents an open-source system integrating a hydrodynamic model with satellite rainfall data (PERSIANN PDIR-Now) and weather prediction data (GFS). It functions as a near real-time Digital Twin (DT) and Early Warning System for high-resolution flood forecasting. Simulated data can be compared with gauge stations in real-time through the model monitoring interface. A proof-of-concept was made by assessing the model capabilities in two case studies. First, the system simulated two consecutive extreme events (hurricanes ETA and IOTA) over the Sula Valley, Honduras, showing fidelity in streamflow responses. Second, the system worked as a DT and EWS to monitor the current and future hydrological states for two periods in 2022 and 2023. Results indicate that satellite data coupled with DT can provide up-to-date system conditions for flood forecasts for regions of lack of data for extreme rainfall events. This tool offered insights to enhance civil protection and societal engagement through warning dissemination against extreme events to build resilience to cope with the increasing magnitude and frequency of disasters in regions with data scarcity.