Search Results (1,622)

This study examines the impacts of climate change on fisheries and aquaculture in Bangladesh, one of the most climate-vulnerable countries in the world. The fisheries and aquaculture sectors contribute significantly to the national GDP and support the livelihoods of 12% of the total population. Using a Critical Literature Review (CLR) approach, peer-reviewed articles, government reports, and official datasets published between 2006 and 2025 were reviewed across databases such as Scopus, Web of Science, FAO, and the Bangladesh Department of Fisheries (DoF). The analysis identifies major climate drivers, including rising temperature, erratic rainfall, salinity intrusion, sea-level rise, floods, droughts, cyclones, and extreme events, and reviews their differentiated impacts on key components of the sector: inland capture fisheries, marine fisheries, and aquaculture systems. For inland capture fisheries, the review highlights habitat degradation, biodiversity loss, and disrupted fish migration and breeding cycles. In aquaculture, particularly in coastal systems, this study reviews the challenges posed by disease outbreaks, water quality deterioration, and disruptions in seed supply, affecting species such as carp, tilapia, pangasius, and shrimp. Coastal aquaculture is also particularly vulnerable to cyclones, tidal surges, and saline water intrusion, with documented economic losses from events such as Cyclones Yaas, Bulbul, Amphan, and Remal. The study synthesizes key findings related to climate-resilient aquaculture practices, monitoring frameworks, ecosystem-based approaches, and community-based adaptation strategies. It underscores the need for targeted interventions, especially in coastal areas facing increasing salinity levels and frequent storms. This study calls for collective action through policy interventions, research and development, and the promotion of climate-smart technologies to enhance resilience and sustain fisheries and aquaculture in the context of a rapidly changing climate. Full article

Rainfall-induced landslide deformation is governed by both intrinsic geological conditions and external dynamic triggers. However, many existing predictive models rely primarily on rainfall inputs, which limits their interpretability and robustness. To address these shortcomings, this study introduces a group-based data augmentation method informed by displacement curve morphology and proposes a multi-slope predictive framework that integrates static geological attributes with dynamic triggering factors. Using monitoring data from 274 sites across China, the framework was implemented with a Temporal Fusion Transformer (TFT) and benchmarked against baseline models, including SVR, XGBoost, and LSTM models. The results demonstrate that group-based augmentation enhances the stability and accuracy of predictions, while the integrated dynamic–static TFT framework delivers superior accuracy and improved interpretability. Statistical significance testing further confirms consistent performance improvements across all groups. Collectively, these findings highlight the framework’s effectiveness for short-term landslide forecasting and underscore its potential to advance early warning systems. Full article

As an effective engineering countermeasure against frost heave damage in seasonally frozen regions, thermal insulation boards (TIBs) were employed in embankments. This study established a test section featuring a thermal insulation–waterproof geotextile embankment in Dingxi, Gansu Province. Temperature and water content at various positions and depths within both the thermal insulation embankment (TIE) and an ordinary embankment (OE) were monitored and compared to analyze the effectiveness of the TIB. Following the installation of the insulation layer, the temperature distribution within the embankment became more uniform. The TIB effectively impeded downward heat transfer (cold energy influx) during the winter and upward heat transfer (heat energy flux) during the warm season. However, the water content within the TIE was observed to be higher than that in the OE, with water accumulation notably occurring at the embankment toe. While the TIB successfully mitigated slope damage and superficial soil frost heave, the waterproof geotextile concurrently induced moisture accumulation at the embankment toe. Consequently, implementing complementary drainage measures is essential. In seasonally frozen areas characterized by dry weather and relatively high winter temperatures, the potential damage caused by concentrated rainfall events to embankments requires particular attention. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

Global Navigation Satellite System (GNSS) meteorology has emerged as a valuable tool for atmospheric monitoring, providing high-resolution, near-real-time data that can significantly improve precipitation nowcasting. This study aims to enhance short-term precipitation forecasting by integrating GNSS-derived Precipitable Water Vapor (PWV)—a key indicator of atmospheric moisture—with traditional meteorological observations. A novel two-step machine learning framework is proposed that combines a Random Forest (RF) model and a Long Short-Term Memory (LSTM) neural network. The RF model first estimates current precipitation based on PWV, surface weather parameters, and auxiliary atmospheric variables. Then, the LSTM network leverages temporal dependencies within the data to predict precipitation for the subsequent hour. This hybrid method capitalizes on the RF’s ability to model complex nonlinear relationships and the LSTM’s strength in handling time series data. The results demonstrate that the proposed approach improves forecasting accuracy, particularly during extreme weather events such as intense rainfall and thunderstorms, outperforming conventional models. By integrating GNSS meteorology with advanced machine learning techniques, this study offers a promising tool for meteorological services, early warning systems, and disaster risk management. The findings highlight the potential of GNSS-based nowcasting for real-time decision-making in weather-sensitive applications. Full article

The integration of Internet of Things (IoT) technologies in agriculture has facilitated real-time environmental monitoring, with low-cost IoT-enabled agrometeorological stations emerging as a valuable tool for climate-smart farming. This systematic review examines low-cost IoT-based weather stations by analyzing their hardware and software components and assessing their potential in comparison to conventional weather stations. It emphasizes their contribution to improving climate resilience, facilitating data-driven decision-making, and expanding access to weather data in resource-constrained environments. The analysis revealed widespread adoption of ESP32 microcontrollers, favored for its affordability and modularity, as well as increasing use of communication protocols like LoRa and Wi-Fi due to their balance of range, power efficiency, and scalability. Sensor integration largely focused on core parameters such as air temperature, relative humidity, soil moisture, and rainfall supporting climate-smart irrigation, disease risk modeling, and microclimate management. Studies highlighted the importance of usability and adaptability through modular hardware and open-source platforms. Additionally, scalability was demonstrated through community-level and multi-station deployments. Despite their promise, challenges persist regarding sensor calibration, data interoperability, and long-term field validation. Future research should explore the integration of edge computing, adaptive analytics, and standardization protocols to further enhance the reliability and functionality of IoT-enabled agrometeorological systems. Full article

Climate change is intensifying hydrological extremes, posing growing threats to the safety and operational reliability of embankment dams worldwide, particularly those in regions susceptible to heavy rainfall and flooding. This study evaluates the overtopping risk for Batu Dam, a critical flood mitigation and water supply structure near Kuala Lumpur, Malaysia, under future climate scenarios, with the aim of informing risk-informed dam safety strategies. Using historical hydrological data (1975–2020) and downscaled climate projections from the CMIP5 database under three Representative Concentration Pathways (RCP4.5, RCP6.0, RCP8.5), we conducted flood routing simulations and probabilistic risk assessments employing the iPRESAS software. Our results demonstrate that the annual probability of overtopping increases substantially under higher-emission scenarios, reaching up to 0.08% by the late century under RCP8.5, driven by increased frequency and intensity of extreme rainfall events. These projections highlight significant spillway capacity limitations and underscore the heightened risk of downstream consequences, including economic losses exceeding RM 200 million and potential loss of life surpassing 2900 individuals in worst-case scenarios. The findings confirm the urgent need for both structural adaptations, such as spillway expansion and crest elevation, and non-structural measures, including enhanced real-time monitoring and early warning systems. This integrated approach offers a robust and replicable framework for strengthening dam safety under evolving climate conditions. Full article

Small landslides, though frequent, are often overlooked despite their significant potential impact on human-affected areas. This study presents an analysis of the Bella Orxeta landslide in Alicante, Spain, a rotational landslide event that occurred in March 2017 following intense and continued rainfall. Utilizing multitemporal datasets, including LiDAR from 2009 and 2016 and drone-based photogrammetry from 2021 and 2023, we generated high-resolution digital terrain models (DTMs) to assess morphological changes, estimate displaced volumes of approximately 3500 cubic meters, and monitor slope activity. Our analysis revealed substantial mass movement between 2016 and 2021, followed by relatively minor changes between 2021 and 2023, primarily related to fluvial erosion. This study demonstrates the effectiveness of UAV and DTM differencing techniques for landslide detection, volumetric analysis, and long-term monitoring in urbanized settings. Beyond its scientific contributions, the Bella Orxeta case offers pedagogical value across academic disciplines, supporting practical training in geomorphology, geotechnical assessment, GIS, and risk planning. It also highlights policy gaps in existing territorial risk plans, particularly regarding the integration of modern monitoring tools for small-scale but recurrent geohazards. Given climate change projections indicating more frequent high-intensity rainfall events in Mediterranean areas, the paper advocates for the systematic documentation of local landslide cases to improve hazard preparedness, urban resilience, and geoscience education. Full article

Atmospheric deposition through rainfall plays a significant role in transporting various anthropogenic contaminants to terrestrial and aquatic ecosystems. However, rainwater’s integrated ionic and molecular composition remains underexplored in semiurban environments. This study provides a comprehensive chemical characterization of rainwater collected during seven precipitation events from February to April 2025 in Athens, Georgia, USA. This semiurban area is characterized by substantial vehicular traffic, seasonal agricultural activities, and ongoing construction, while lacking significant industrial emissions. Targeted spectrophotometric analyses revealed heightened concentrations of nitrate (ranging from 2.0 to 4.3 mg/L), sulfate (17 to 26 mg/L), and phosphate (2.4 to 3.1 mg/L), with peak concentrations observed during high-intensity rainfall events. These findings are consistent with enhanced wet scavenging of atmospheric emissions. Concurrently, both targeted and non-targeted gas chromatography-mass spectrometry (GC-MS) analyses identified a diverse array of organic pollutants in the rainwater, including organophosphate, organochlorine, and triazine pesticides; polycyclic aromatic hydrocarbons (PAHs); plasticizers; flame retardants; surfactant degradation products; and industrial additives such as bisphenol A, triclosan, and nicotine. Furthermore, several legacy contaminants, such as organochlorines, were detected alongside currently utilized compounds, including glyphosate and its metabolite aminomethylphosphonic acid (AMPA). The concurrent presence of elevated anion and organic pollutant levels during significant storm events suggests that atmospheric washout can be the primary deposition mechanism. These findings underscore the capability of semiurban atmospheres to accumulate and redistribute complex mixtures of pollutants through rainfall, even in the absence of large-scale industrial activity. The study emphasizes the importance of integrated ionic and molecular analyses for uncovering concealed pollution sources. It highlights the potential of rainwater chemistry as a diagnostic tool for monitoring atmospheric contamination in urbanizing environments. Full article

Frequent and severe droughts intensify water scarcity in arid and semi-arid regions, creating an urgent need for alternative water resources in agriculture. Treated wastewater (TWW) has emerged as a sustainable option; however, its long-term use may alter soil properties and pose risks if not carefully managed. This study tested the hypothesis that long-term TWW irrigation increases soil salinity, alters fertility, and affects microbial quality, with rainfall partially mitigating these effects. Soil samples (n = 96 at each time point) were collected from two calcareous soils in Jordan, silt loam (Mafraq) and silty clay loam (Ramtha), under four treatments (control and 2, 5, and 10 years of TWW irrigation) at three depths (0–30, 30–60, and 60–90 cm). Sampling was conducted at two intervals, before and after rainfall, to capture the seasonal variation. Soil indicators included the pH, electrical conductivity (EC), sodium (Na⁺), chloride (Cl⁻), calcium (Ca²⁺), magnesium (Mg²⁺), exchangeable sodium percentage (ESP), sodium adsorption ratio (SAR), organic matter (OM), total nitrogen (TN), and microbial parameters (total coliforms (TC), fecal coliforms (FC), and Escherichia coli). Data were analyzed using a linear mixed-effects model with repeated measures, and significant differences were determined using Tukey’s Honest Significant Difference (HSD) test at p < 0.05. The results showed that rainfall reduced Na⁺ by 70%, Cl⁻ by 86%, EC by 73%, the ESP by 28%, and the SAR by 30%. Furthermore, the TC and FC concentrations were diminished by almost 96%. Moderate TWW irrigation (5 years) provided the most balanced outcomes across both sites. This study provides one of the few long-term field-based assessments of TWW irrigation in semi-arid calcareous soils of Jordan, underscoring its value in mitigating water scarcity while emphasizing the need for monitoring to ensure soil sustainability. Full article

Climate change is intensifying droughts, heatwaves, dust storms, and rainfall variability across Eastern Europe, undermining yields and soil stability. In Ukraine, decades of underinvestment and wartime damage have led to widespread degradation of field shelterbelts, while the adoption of agroforestry remains constrained by tenure ambiguity, fragmented responsibilities, and limited access to finance. This study develops a policy-and-technology framework to restore agroforestry at scale under severe fiscal and institutional constraints. We apply a three-stage approach: (i) a national baseline (post-1991 legislation, statistics) to diagnose the biophysical and legal drivers of shelterbelt decline, including wartime damage; (ii) a comparative synthesis of international support models (governance, incentives, finance); and (iii) an assessment of transferability of digital monitoring, reporting, and verification (MRV) tools to Ukraine. We find that eliminating tenure ambiguities, introducing targeted cost sharing, and enabling access to payments for ecosystem services and voluntary carbon markets can unlock financing at scale. A digital MRV stack—Earth observation, UAV/LiDAR, IoT sensors, and AI—can verify tree establishment and survival, quantify biomass and carbon increments, and document eligibility for performance-based incentives while lowering transaction costs relative to field-only surveys. The resulting sequenced policy package provides an actionable pathway for policymakers and donors to finance, monitor, and scale shelterbelt restoration in Ukraine and in similar resource-constrained settings. Full article

This study investigates slope stability in an open-pit mining area by integrating engineering geological surveys, field investigations, and laboratory rock mechanics tests. A coordinated multimethod analysis was carried out using finite element-based numerical simulations from both two-dimensional and three-dimensional perspectives. The integrated approach revealed deformation patterns across the slopes and established a multiscale analytical framework. The results indicate that the slope failure modes primarily include circular and compound types, with existing step slopes showing a potential risk of wedge failure. While the designed slope meets safety requirements under three working conditions overall, the strongly weathered layer in profile XL3 requires a slope angle reduction from 38° to 37° to comply with standards. Three-dimensional simulations identify the main deformations in the middle-lower sections of the western area and zones B and C, with faults located at the core of the deformation zone. Rainfall and blasting vibrations significantly increase surface tensile stress, accelerating deformation. Although wedges in profiles XL1 and XL4 remain generally stable, coupled blasting–rainfall effects may still induce potential collapse in fractured areas, necessitating preventive measures such as concrete support and bolt support, along with real-time monitoring to dynamically optimize reinforcement strategies for precise risk control. Full article

Inundation dynamics in low-lying deltas are becoming increasingly important to monitor due to the impacts of climate change and human alterations to hydrological systems, which disrupt natural inundation patterns. In the Nemunas River Delta, where seasonal and extreme floods impact agricultural and natural landscapes, we used Sentinel-1 synthetic aperture radar (SAR) imagery (2015–2019), validated with drone data, to map flood extents. SAR provides consistent, 10 m resolution data unaffected by cloud cover, while drone imagery provides high-resolution (10 cm) data at 90 m flight height for validation during SAR acquisitions. Results revealed peak inundation during spring snowmelt and colder months, with shorter, rainfall-driven summer floods. Approximately 60% of inundated areas were low-lying agricultural fields, which experienced prolonged waterlogging due to poor drainage and soil degradation. Inundation duration was shaped by lithology, land cover, and topography. A consistent 5–10-day lag between peak river discharge and flood expansion suggests discharge data can complement SAR when imagery is unavailable. This study confirms SAR’s value for flood mapping in cloud-prone, temperate regions and highlights its scalability for monitoring flood-prone deltas where agriculture and infrastructure face increasing climate-related risks. Full article

Soil moisture content is an important determinant of crop productivity, especially in agricultural systems that are dependent on rainfall. Climate variability has introduced water management challenges for smallholder farmers in Southern Africa. The emergence of unmanned aerial vehicle (UAV)-borne remote sensing offers modern solutions for monitoring soil moisture, plant health and overall crop productivity in real-time. This study evaluated the utility of UAV-acquired data in conjunction with random forest regression in predicting soil moisture content and chlorophyll across different growth stages of taro. The estimation models achieved R² values up to 0.90 with rRMSE as low as 1.25%, demonstrating the robust performance of random forest in concert with different spectral datasets in estimating soil moisture and chlorophyll. Correlation analysis confirmed the association between these two variables, with the strongest correlation observed during the vegetative stage (r = 0.81, p < 0.05) and the weakest during the late vegetative stage (r = 0.78, p < 0.05). The results showed that UAV bands were crucial in predicting soil moisture and chlorophyll across all stages. These results demonstrate the utility of remote sensing, particularly UAV-borne sensors, in monitoring crop productivity in smallholder farms. By employing UAV-borne sensors, farmers can improve on-farm water management and make better and more informed decisions. Full article

Background: Polluted rivers may become reservoirs of antibiotic-resistant Escherichia coli (AREc), raising concerns about environmental health. While monitoring is crucial for recognizing their incidence and evaluating mitigation solutions, current approaches are limited due to high costs, labor-intensive methods, and a lack of standardized indicators. This study aims to identify the priority AREc as the monitoring target and evaluate the applicability of the β-glucuronidase enzyme detection method (MPR Method) as an alternative rapid method for profiling AREc in the Upper Citarum River. Methods: River water sampling was conducted along the river during two periods with varying rainfall levels. Total Escherichia coli (TEc) and twelve types of antibiotic-resistant Escherichia coli (AREc) were measured simultaneously by the Agar Method and the β-D-Glucuronidase detection (MPR Method). Results: Statistical data analyses indicate that Total Escherichia coli (TEc) concentrations in the Upper Citarum River increase during periods of higher rainfall (𝓍 = 2558 ± 360 CFU/mL). Erythromycin-resistant Escherichia coli dominates in both periods (Period I 𝓍 = 57.6 ± 25.9%, Period II 𝓍 = 49.96 ± 29.5%). However, tetracycline-resistant Escherichia coli and Extended-Spectrum β-lactamase-producing Escherichia coli (ESBL-Ec) are the most suitable indicators for AREc concentration due to their consistency and correlation with other AREc types. The MPR method achieved an accuracy of up to 87.2%, a sensitivity of 67.4%, and a specificity of 94%. Conclusion: The MPR Method was considered a better alternative for the AREc screening method, particularly in a high bacterial load aquatic environment. Full article