Search Results (2,077)

This study aims to investigate the effects of Chan-Hom (2015) typhoon-induced variations in enthalpy flux (EF) and moisture flux (MF) on intensity variations and rainfall. Chan-Hom (2015) made landfall at Zhoushan, then changed its direction and moved towards Korea. This analysis used ERA5 reanalyzed data, encompassing daily surface latent and sensible heat flux, along with wind measurements at a height of 10 m. Furthermore, wind components and specific humidity data from the 1000–200 hPa level in ERA5 were utilized to compute the MF and MF convergence, in accordance with the equations outlined in the methodology. This study examines the correlation among typhoon intensity, precipitation, MF, and EF. The mechanism by which Typhoon Chan-Hom has caused a decline in sea surface temperature (SST) was analyzed. Typhoons need a higher EF that can affect them before landfall to maintain their intensity. The highest LHF was observed (340 W/m²) prior to typhoon landfall, indicating that LHF responds to intensity-induced wind during Chan-Hom. Typhoon-induced rainfall is mainly controlled by the MF convergence, rather than the typhoon intensity. The spatial and temporal distributions of MF and MF convergence (MFC) during typhoon formation to landfall reveal that the symmetric MFC is dominated by typhoon intensity; a symmetrical structure is observed when the intensity is high. MFC includes wind convergence and moisture advection. Wind convergence dominates the MFC during typhoons, but moisture advection forms at the eyewall. MF during the typhoon’s landfall can relate to the amount of rainfall that occurred over the land. However, the rainfall pattern changed after landfall, and the typhoon changed its direction. SST cooling observed in the study area is mainly due to the upwelling process with strong cyclonic winds. Full article

Mudstone badlands are critical hotspots of erosion and sediment yield, and their rapid morphological changes serve as an ideal site for studying erosion processes. This study used high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry to monitor erosion patterns on a mudstone badland platform in southwestern Taiwan over a 22-month period. Five UAV surveys conducted between 2017 and 2018 were processed using Structure-from-Motion photogrammetry to generate time-series digital surface models (DSMs). Topographic changes were quantified using DSMs of Difference (DoD). The results reveal intense surface lowering, with a mean erosion depth of 34.2 cm, equivalent to an average erosion rate of 18.7 cm yr⁻¹. Erosion is governed by a synergistic regime in which diffuse rain splash acts as the dominant background process, accounting for approximately 53% of total erosion, while concentrated flow drives localized gully incision. Morphometric analysis shows that erosion depth increases nonlinearly with slope, consistent with threshold hillslope behavior, but exhibits little dependence on the contributing area. Plan and profile curvature further influence the spatial distribution of erosion, with enhanced erosion on both strongly concave and convex surfaces relative to near-linear slopes. The gully network also exhibits rapid channel adjustment, including downstream meander migration and associated lateral bank erosion. These findings highlight the complex interactions among hillslope processes, gully dynamics, and base-level controls that govern badland landscape evolution and have important implications for erosion modeling and watershed management in high-intensity rainfall environments. Full article

This study develops a statistical and technological framework to analyze extreme rainfall in Colima, Mexico, by integrating historical precipitation records, probabilistic modeling, and spatial visualization. Using data from CONAGUA meteorological stations, we identify high-intensity rainfall events and model their recurrence using the Generalized Extreme Value (GEV) distribution to estimate key return periods. The results support flood-risk assessment and territorial planning in Colima. Spatial interpolation was performed in Python (version 3.13), and QGIS (version 3.38) produces exceedance maps that illustrate geographic variations in rainfall intensity across the state. These exceedance maps reveal a consistent spatial pattern, with the northern and western areas of Colima experiencing the highest frequencies of extreme events. Based on these results, the integration of real-time sensor technologies and satellite observations may improve flood monitoring and risk management frameworks. Full article

In sub-Saharan Africa, land degradation and climate change continue to undermine agricultural productivity by reducing soil productivity and water availability. This review identifies soil and water conservation technologies successfully applied in climatically analogous regions of sub-Saharan Africa with the aim of informing effective technology transfer to Senegal, particularly Sédhiou and Tambacounda. Using K-means clustering on WorldClim bioclimatic variables, 35 comparable countries were identified, of which 17 met inclusion criteria based on data availability and ≥60% climatic similarity. Eighty-five technologies were documented and assessed for their compatibility across rainfall patterns, land gradients, and uses, with 12 emerging as consistently effective. Quantitative evidence shows that zai/tassa pits, stone bunds, and half-moons increase crop yields by 50–200%, while stone bunds and mulching reduce runoff by up to 80% and improve soil moisture retention. Terracing and tied-ridging were also linked to higher water-use efficiency, with tied-ridging increasing soil moisture by 13%. Burkina Faso, Kenya, and Malawi lead in adoption and diversity, whereas Senegal lags due to institutional gaps, limited funding, and weak extension systems. These technologies offer a readily available, evidence-based toolkit for building agricultural resilience in Senegal. However, their successful adoption requires stronger policy integration, stakeholder empowerment, cross-border learning, and private-sector engagement. Full article

Microplastics (MPs) and nanoplastics (NPs) have emerged as pervasive pollutants across different aquatic systems on a global basis, yet integrated assessments linking wastewater, riverine, and marine environments remain scarce. The present study provides the first comprehensive evaluation of MPs in three interconnected aquatic matrices of Northern Greece, namely surface seawater from the Thermaic Gulf, surface freshwater from the Axios River, and influent and effluent wastewaters from the Thessaloniki WWTP (Sindos). During two sampling periods spanning late 2023 and spring 2024, suspected MPs were isolated, morphologically classified by stereomicroscopy, and chemically characterized through pyrolysis–gas chromatography/mass spectrometry (Py–GC/MS). MPs were ubiquitously detected in all substrates, exhibiting distinct spatial and compositional patterns. Seawater samples displayed moderate concentrations (1.5–4.8 items m⁻³) dominated by fibers and fragments, while riverine samples contained slightly higher levels (0.5–2.5 items m⁻³), enriched in fibrous forms and polyolefins (PE, PP). Wastewater influents showed the highest MP abundance (78–200 items L⁻¹; 155.6–392.3 µg L⁻¹), decreasing significantly in effluents (11–44 items L⁻¹; 27.8–74.3 µg L⁻¹), corresponding to a removal efficiency of 81–87.5%, being the first indicative removal efficiencies in a Greek WWTP. Among the different polymers detected, polyethylene, polypropylene, and poly(ethylene terephthalate) were identified as the most prevalent polymers across all matrices. Interestingly, a shift toward smaller size classes (125–500 µm) in effluents indicated in-plant fragmentation processes, while increased concentrations during December coincided with increased rainfall, highlighting the influence of hydrological conditions on MP fluxes. The combined morphological and polymer-specific approach provides a holistic zunderstanding of MP transport from inland to marine systems, establishing essential baseline data for Mediterranean environments and reinforcing the need for integrated monitoring and mitigation strategies. Full article

Availability of irrigation water during growing seasons in the Republic of South Africa (RSA) remains a significant concern. Persistent droughts and unpredictable rainfall patterns attributed to climate change, coupled with an increasing population, have exacerbated irrigation water scarcity. Globally, treated wastewater has been utilised as an irrigation water source; however, despite global advances in the usage of treated wastewater, its suitability for irrigation in RSA remains a contentious issue. Considering this uncertainty, this review article aims to unravel the South African scenario on the suitability of treated wastewater for irrigation purposes and highlights the potential environmental impacts and public health risks. The review synthesised literature in the last two decades (2000–present) using Web of Science, ScienceDirect, ResearchGate, and Google Scholar databases. Findings reveal that treated wastewater can serve as a viable irrigation source in the country, enhancing various soil parameters, including nutritional pool, organic carbon, and fertility status. However, elevated levels of salts, heavy metals, and microplastics in treated wastewater resulting from insufficient treatment of wastewater processes may present significant challenges. These contaminants might induce saline conditions and increase heavy metals and microplastics in soil systems and water bodies, thereby posing a threat to public health and potentially causing ecological risks. Based on the reviewed literature, irrigation with treated wastewater should be implemented on a localised and pilot basis. This review aims to influence policy-making decisions regarding wastewater treatment plant structure and management. Stricter monitoring and compliance policies, revision of irrigation water standards to include emerging contaminants such as microplastics, and intensive investment in wastewater treatment plants in the country are recommended. With improved policies, management, and treatment efficiency, treated wastewater can be a dependable, sustainable, and practical irrigation water source in the country with minimal public health risks. Full article

Non-destructive monitoring of fruit ripening is essential for optimising harvest time, yet its application to tropical viticulture remains largely unexplored. This study evaluated in situ chlorophyll a fluorescence as a non-invasive physiological marker to track berry development and metabolic maturation in two table grape cultivars (Vitis labrusca L. var. Niagara Rosada and var. Romana) under tropical field conditions, characterised by the latitude position, absence of chilling-induced dormancy, and variable rainfall during ripening. Berries’ fluorescence parameters (Fo, Fm, Fv and Fv/Fm) were monitored weekly from the pea-size stage to commercial harvest (67–123 days after pruning) using a portable modulated fluorometer, along with chlorophyll and quality trait measurements. A decline in fluorescence parameters during maturation coincided with chlorophyll degradation and the accumulation of glucose and fructose. The maximum quantum yield of PSII (Fv/Fm) remained stable (≈0.75) throughout development, indicating sustained photochemical efficiency despite chloroplast disassembly. Significant correlations (r > 0.80) were established between fluorescence parameters and key maturity indices, with distinct cultivar-specific patterns evident between the NR and RM cultivars. Therefore, chlorophyll a fluorescence provided a reliable, portable, non-destructive tool for monitoring ripening dynamics and estimating quality parameters in table grapes, offering practical advantages for tropical viticulture where environmental variability demands flexible monitoring. Full article

Hydrological behavior plays a critical role in seasonally dry forest ecosystems, as it underpins water availability for multiple productive activities, including forestry, agriculture, grazing, and urban supply. This study evaluated the hydrological effects of thinning treatments in a semi-arid oak–pine forest of Chihuahua, Mexico, using a Before–After–Control–Impact (BACI) design. Three Micro-catchments (MC) with initially comparable tree density and canopy cover were monitored during the rainy seasons of 2018 (pre-thinning) and 2019 (post-thinning). Thinning treatments were applied at 20% and 60% canopy cover in two MC, while a third remained unthinned as a 100% control. Precipitation and surface runoff were recorded at the event scale, and data were analyzed using Weibull probability models with a log link to capture the frequency and magnitude of runoff events. Precipitation patterns were broadly comparable across years, although 2018 included an extreme storm event (59 mm). In contrast, runoff volumes in 2019 were lower despite marginally higher seasonal rainfall, reflecting the absence of large storms. Statistical modeling indicated that for each additional millimeter of precipitation, mean runoff increased by approximately 12%, although thinning significantly altered baseline conditions. Relative to 2018, mean runoff ratios were 0.087 in the 100% canopy catchment, 0.296 in the 60% treatment, and 0.348 in the 20% treatment, suggesting that reduced canopy cover retained proportionally more runoff than the control. BACI contrasts confirmed that thinned catchments maintained higher proportions of runoff than the unthinned control, although statistical significance was marginal for the 20% canopy treatment. Overall, the study provides ecohydrological insights relevant to the management of semi-arid forest ecosystems. Full article

Background: Rainfall-induced landslides are a widespread and destructive geological hazard that resist precise prediction. They pose serious threats to human lives and property, ecological stability, and socioeconomic development. Methods: To address the challenges in mitigating rainfall-induced landslides in high-altitude mountainous regions, this study proposes a digital twin framework that couples multiple physical fields and is based on the spherical discrete element method. Results: Two-dimensional simulations identify a trapezoidal stress distribution with inward-increasing stress. The stress increases uniformly from 0 kPa at the surface to 210 kPa in the interior. The crest stress remains constant at 1.8 kPa under gravity, whereas the toe stress rises from 6.5 to 14.8 kPa with the slope gradient. While the stress pattern persists post-failure, specific magnitudes alter significantly. This study pioneers a three-dimensional close-packed spherical discrete element method, achieving enhanced computational efficiency and stability through streamlined contact mechanics. Conclusions: The proposed framework utilizes point-contact mechanics to simplify friction modeling, enhancing computational efficiency and numerical stability. By integrating stress, rainfall, and seepage fields, we establish a coupled hydro-mechanical model that enables real-time digital twin mapping of landslide evolution through dynamic parameter adjustments. Full article

Climate change and irregular precipitation patterns have increasingly threatened groundwater sustainability in semi-arid regions like the Eastern Mediterranean. Specifically, in coastal Syria, the lack of quantitative understanding regarding aquifer recharge mechanisms hinders effective water resource management. To address this, this study investigates the dynamic relationship between rainfall and groundwater levels in the Al-Hsain Basin coastal plain using 48 months of monitoring data (2020–2024) from 35 wells. We employed a unified analytical framework combining statistical methods (correlation, regression) with advanced time–frequency techniques (Wavelet Coherence) to capture recharge behavior across diverse Quaternary, Neogene, and Cretaceous strata. The results indicate strong climatic control on groundwater dynamics, particularly in shallow Quaternary wells, which exhibit rapid recharge responses (lag < 1 month). In contrast, deeper aquifers showed delayed and buffered responses. A dual-variable model incorporating temperature significantly improved prediction accuracy (R² = 0.97), highlighting the role of evapotranspiration. These findings provide a transferable diagnostic framework for identifying recharge zones and supporting adaptive groundwater governance in data-scarce semi-arid environments. Full article

Against the backdrop of intensifying global climate change and expanding human encroachment into mountainous regions, landslides have increased markedly in both frequency and destructiveness, emerging as a key risk to socio-ecological security and development in mountain areas. Rigorous assessment and forward-looking prediction of landslide disaster vulnerability (LDV) are essential for targeted disaster risk reduction and regional sustainability. However, existing studies largely center on landslide susceptibility or risk, often overlooking the dynamic evolution of adaptive capacity within affected systems and its nonlinear responses across temporal and spatial scales, thereby obscuring the complex mechanisms underpinning LDV. To address this gap, we examine Yunnan Province, a landslide-prone region of China where intensified extreme rainfall and the expansion of human activities in recent years have exacerbated landslide risk. Drawing on the vulnerability scoping diagram (VSD), we construct an exposure–sensitivity–adaptive capacity assessment framework to characterize the spatiotemporal distribution of LDV during 2000–2020. We further develop a multi-model, multi-scale integrated prediction framework, benchmarking the predictive performance of four machine learning algorithms—backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and XGBoost—across sample sizes ranging from 2500 to 360,000 to identify the optimal model–scale combination. From 2000 to 2020, LDV in Yunnan declined overall, exhibiting a spatial pattern of “higher in the northwest and lower in the southeast.” High-LDV areas decreased markedly, and sustained enhancement of adaptive capacity was the primary driver of the decline. At approximately the 90,000-cell grid scale, XGBoost performed best, robustly reproducing the observed spatiotemporal evolution and projecting continued declines in LDV during 2030–2050, albeit with decelerating improvement; low-LDV zones show phased fluctuations of “expansion followed by contraction”, whereas high-LDV zones continue to contract northwestward. The proposed multi-model, multi-scale fusion framework enhances the accuracy and robustness of LDV prediction, provides a scientific basis for precise disaster risk reduction strategies and resource optimization in Yunnan, and offers a quantitative reference for resilience building and policy design in analogous regions worldwide. Full article

This study examines the post-war evolution of groundwater quality in Mosul by evaluating the seasonal and annual behavior of physicochemical parameters and heavy metals, while differentiating the responses of shallow and deep aquifers and determining whether groundwater conditions during the early recovery period (2022–2023) indicate natural improvement or continued deterioration. Groundwater samples from shallow (W5–W8) and deep (W1–W4) wells were collected across four sampling campaigns representing both wet and dry seasons. Shallow wells exhibited marked seasonal increases, with pH, electrical conductivity (EC), and total dissolved solids (TDS) increasing during the dry season, driven by evaporation and limited recharge. Nutrient concentrations (PO₄³⁻, NO₃⁻, SO₄²⁻) showed similar seasonal rises but declined slightly in 2023 following reduced rainfall. Heavy metals (Cd, Pb, Cr, Ni, Zn) displayed pronounced seasonal peaks in the wet season and higher annual averages in 2023, suggesting delayed mobilization from contaminated soils. In contrast, deep wells remained relatively stable, reflecting the buffering capacity of deeper geological formations. Statistical analyses supported these patterns: shallow wells demonstrated significant seasonal variability (p < 0.05) across most parameters, whereas deep wells exhibited limited seasonal differences and no significant annual variation. These findings indicate that shallow aquifers—particularly those constructed during the conflict—are more vulnerable to post-war environmental stresses, while deeper aquifers retain greater resilience. Overall, the study underscores progressive degradation of shallow groundwater linked to post-conflict conditions and highlights the need for sustained monitoring, stricter regulation of groundwater use, and targeted remediation strategies to protect drinking and irrigation resources in conflict-affected regions. These insights are crucial for developing sustainable groundwater management strategies in post-war urban environments. Full article

To mitigate the frequent occurrence of rear-end collisions on urban expressways under rainy weather conditions, firstly, accident risk levels were classified using traffic conflict indicators. Secondly, three machine learning models were employed to predict the accident severity across different scenarios. Furthermore, key influencing factors of rear-end collisions were identified and analyzed based on SHAP values. Case studies were conducted by simulating vehicle trajectory data under light, moderate, and heavy rain scenarios, using an open urban expressway dataset and car-following parameters for rainy conditions. Next, the Modified Time-to-Collision (MTTC) metric was calculated. Risk thresholds for low-, medium-, and high-risk levels were established for each rainfall category using percentile-based cumulative distribution analysis. Finally, real-time risk prediction under the three rainfall scenarios was conducted using XGBoost, LightGBM, and Random Forest models. The model performances were evaluated in terms of accuracy, recall, precision, and AUC. Overall, the study finds that the LightGBM model achieves the highest predictive capability, with AUC values exceeding 0.78 under all weather conditions. Moreover, the study concludes that factors ranked by SHAP values reveal that the minimum distance has the greatest influence in light rain scenarios. As rainfall intensity increases, the influences of minimum headway time and average vehicle speed are found to grow, highlighting an interaction pattern characterized by “speed-distance-flow” coupling. Full article

Flood hazards in arid oil-producing regions result from both natural hydrological processes and terrain changes due to land subsidence. In the Yibal field in northern Oman, long-term hydrocarbon extraction has caused measurable ground deformation, altering surface gradients and drainage patterns. This study presents a deformation-adjusted flood hazard assessment by integrating a 2013 photogrammetric DEM with a 2023 subsidence-corrected DEM derived from multi-temporal PS-InSAR observations (RADARSAT-2 and TerraSAR-X). Key hydrological indicators—including slope, drainage networks, Height Above Nearest Drainage (HAND), floodplain depth, Curve Number, and extreme precipitation from the wettest monthly rainfall in a 10-year archive—were recalculated for both years. Flood hazard maps for 2013 and 2023 were generated using an AHP-based multi-criteria framework across five hydrologically motivated scenarios. Results indicate that while the total area of high- and very-high-hazard zones changed only slightly in most scenarios (within ±6%), these zones shifted into subsidence-affected depressions, reflecting deformation-driven redistribution of flood-prone areas. Low-hazard zones grew most significantly, especially in Scenarios S2–S4, with increases of 160–320% compared to 2013, while moderate-hazard areas showed smaller but consistent growth. Floodplain-dominated conditions (S5) produced the most pronounced nonlinear response, with a substantial increase in very low hazard and localized concentration of very high hazard in areas of deepest subsidence. Geomorphic analysis using the Geomorphic Flood Index (GFI) shows deepening of flow pathways and expansion of geomorphic depressions between 2013 and 2023, supporting the modeled redistribution of hazards. These findings demonstrate that even moderate subsidence can significantly alter hydrological susceptibility and underscore the importance of incorporating deformation-adjusted terrain modeling into flood hazard assessments in petroleum fields and other subsidence-prone areas. Full article

This study investigated the site-specific ionic composition and wet deposition loads of rainwater collected from eight actively cultivated agricultural regions across South Korea, with the aim of quantifying spatial and seasonal variability and interpreting how regional agricultural characteristics and surrounding site conditions influence major ion concentrations and deposition patterns. Rainfall samples were obtained using automated samplers and analyzed via high-performance ion chromatography for major cations (Na⁺, $\mathrm{N}{\mathrm{H}}_4^{+}$, K⁺, Ca²⁺, Mg²⁺) and anions (Cl⁻, $\mathrm{N}{\mathrm{O}}_3^{-}$, $\mathrm{S}{\mathrm{O}}_4^{2-}$, $\mathrm{N}{\mathrm{O}}_2^{-}$). The results revealed significant seasonal fluctuations in ion loads, with $\mathrm{N}{\mathrm{H}}_4^{+}$ (peak 1.13 kg/ha) and K⁺ (peak 0.25 kg/ha) reaching their highest levels during summer due to increased fertilizer use and crop activity. Conversely, Cl⁻ peaked in winter (2.11 kg/ha in December), particularly in coastal regions, likely influenced by de-icing salts and sea-salt aerosols. Correlation analysis showed a strong positive association among $\mathrm{N}{\mathrm{H}}_4^{+}$, $\mathrm{N}{\mathrm{O}}_3^{-}$, and $\mathrm{S}{\mathrm{O}}_4^{2-}$ (r = 0.89 and r = 0.84, respectively), indicating shared atmospheric transformation pathways from agricultural emissions. Ternary diagram analysis further revealed regional distinctions: coastal regions such as Gimhae and Muan exhibited Na⁺ and Cl⁻ dominance, while inland areas like Danyang and Hongcheon showed higher proportions of Ca²⁺ and Mg²⁺, reflecting differences in aerosol sources, land use, and local meteorological conditions. These findings underscore the complex interactions between agricultural practices, atmospheric processes, and local geography in shaping rainwater chemistry. The study provides quantitative baseline data for evaluating non-point source pollution and developing region-specific nutrient and soil management strategies in agricultural ecosystems. Full article