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Dust storms are events that have significant effects on plants, ecosystems, and socio-economic conditions. This study offers an analysis of research on how dust storms impact plant life, highlighting trends, challenges, and future directions. The analysis shows an increase in research output during the “High-Output Period” (2018–2024), with a peak of 105 articles in 2023, driven by advancements in remote sensing technologies and increased global collaboration. Key findings suggest that dust storms harm plants by causing mechanical damage, reducing photosynthesis, and depleting soil nutrients, which negatively impacts plant health. Plants play a critical role in mitigating dust storms by stabilizing soil and reducing the spread of dust particles. However, the study also points out challenges such as the complexity of modeling interactions between dust storms and vegetation, regional variations, and data constraints. Future research should concentrate on creating comprehensive models that consider socio-economic factors, conducting long-term studies to understand lasting effects, and expanding the use of advanced remote sensing technologies. It is also essential to explore underexplored regions and investigate how dust storms affect economic conditions to develop effective management strategies comprehensively. Understanding how climate change affects the dynamics of dust storms will be critical to forecast patterns and advise on adaptation strategies.

This study explores the application of the Rain-on-Grid approach within the two-dimensional (2D) Hydrologic Engineering Center’s River Analysis System (HEC-RAS, version 6.6) for a selected area (~ 230 km2) of the Low Calore River catchment in Southern Italy, which was heavily hit by an extreme rainfall event on October 14–15, 2015. This event, lasting about 17 h, triggered a range of geo-hydraulic phenomena, including extensive flooding of the Calore River, with physical damage to the railway infrastructure. The hydrodynamic model was used to reconstruct the effects of the observed rainfall event by including relevant processes such as spatially distributed rainfall, upstream discharge input, infiltration losses, and flow propagation across hillslopes and within the Calore River. The results demonstrate that 2D Rain-on-Grid HEC-RAS simulations, which account for the minor tributary network, can reproduce realistic correlations with recorded damage to linear transport infrastructure. The comparison with a traditional fluvial flooding approach, where a given discharge hydrograph is used as the only input to the hydrodynamic model, shows that the traditional approach fails to evaluate the activation of the minor tributary network, leading to an underestimation of potential infrastructure damages, and the inability to explain observed damages. These results suggest that hazard maps should explicitly model pluvial and compound pluvial-fluvial flooding when assessing risks to transportation networks. However, the findings also highlight certain limitations, including the need for more detailed and spatially distributed input data and increased computational time.

The Himalayas, often termed the Third Pole, host the largest expanse and volume of permanent ice and permafrost outside the Polar Regions. The ongoing recession of glaciers, driven by climate change, has led to the formation of new glacial lakes through the accumulation of meltwater between the retreating glacier and its frontal moraine, as well as the expansion and merging of existing lakes. This process increases the risk of glacial lake outburst floods (GLOFs), posing a significant threat to downstream communities. In Uttarakhand, India, more than a thousand glacial lakes present considerable challenges for monitoring and risk assessment. Hence, it is essential to identify and prioritize potentially dangerous glacial lakes (PDGLs) for effective risk management. Using Sentinel-2 satellite imagery, 426 glacial lakes with a surface area greater than 1,000 m² were inventoried; these lakes cover a cumulative area of ~ 3.34 × 106 m2. Based on their vulnerability to the GLOF hazard, 25 out of 426 lakes were classified as PDGLs. Lake vulnerability was assessed using a parameter-weighting approach, considering factors such as volume, expansion, dam condition, distance from settlements, and upstream and downstream conditions of the valley. These lakes were further classified into three hazard categories: six lakes in Category A (most hazardous), six lakes in Category B (moderately hazardous), and 13 lakes in Category C (least hazardous). Alaknanda and Dhauliganga Valleys contain 9 and 6 PDGLs, respectively, making them most sensitive to GLOF hazards. These findings underscore the urgent need for focused monitoring and mitigation strategies to address GLOF risks in the region.

The worldwide threat of coastal flood hazard due to sea level rise (SLR) and climate change poses a significant risk to socio-economic assets and coastal communities. Existing gaps in hazard categorisation, exposure assessment and vulnerability analysis suggest critical evaluations for the existing methodologies. Existing coastal flood risk methodologies, particularly at the microscale, require the evaluation of diverse methods to improve the representation of hazards, refining exposure and vulnerability analysis for decision-making and climate adaptation planning. This review elaborates on advancing precise, localised flood risk evaluation methods, integrating diverse datasets, hazard scenarios, and multi-dimensional vulnerability dimensions, thereby enhancing resilience and informed decision-making for coastal communities worldwide. Quantitative analyses were conducted in Excel, and qualitative assessments employed descriptive and narrative methods following PRISMA 2020. Results categorised risk assessment methods using tables and graphical tools like bar and pie charts to highlight trends. Key findings highlight the collection and integration of socioeconomic data, high-resolution digital elevation models (DEM), consideration of local characteristics such as topography and beach slope variations, range of flood parameters, hybrid approach by combining deterministic and probabilistic models, use of both trend extrapolation and scenario prediction techniques, comprehending interactions between storm surges and wave setup, thorough vulnerability assessment with a more balanced emphasis on physical, functional, socioeconomic, and geomorphological factors is needed. In addition to physical vulnerability, social, economic, and environmental vulnerability should also receive more attention. Future directions should strive for enhancing data integration and collecting, applying hybrid modelling approaches, extending the range of flood parameters, and taking local conditions into account. Furthermore, all components of receptors and vulnerability assessment should receive equal attention as they are also significant. Limitations include sparse data in some regions and incomplete consideration of socio-economic and environmental vulnerabilities. The review calls for methodological refinement and interdisciplinary collaboration to improve flood risk frameworks, particularly at the micro-scale.