Search Results (361)

Wetlands in dryland regions are rapidly degrading under the combined effects of climate change and human regulation, yet long-term, seasonally resolved assessments of surface water extent (SWE) and its dynamics remain scarce. Here, we map and analyze seasonal surface water extent (SWE) over the period 2000–2024 in the Ile River Delta (IRD), south-eastern Kazakhstan, using Landsat TM/ETM+/OLI data within the Google Earth Engine (GEE) framework. We integrate multiple indices using the modified Normalized Difference Water Index (mNDWI), Automated Water Extraction Index (AWEI) variants, Water Index 2015 (WI2015), and Multi-Band Water Index (MBWI) with dynamic Otsu thresholding. The resulting index-wise binary water maps are merged via ensemble agreement (intersection, majority, union) to delineate three SWE regimes: stable (persists most of the time), periodic (appears regularly but not in every season), and ephemeral (appears only occasionally). Validation against Sentinel-2 imagery showed high accuracy F1-Score/Overall accuracy (F1/OA ≈ 0.85/85%), confirming our workflow to be robust. Hydroclimatic drivers were evaluated through modified Mann–Kendall (MMK) and Spearman’s (r) correlations between SWE, discharge (D), water level (WL), precipitation (P), and air temperature (AT), while a hybrid ensemble–occurrence framework was applied to identify degradation and transition patterns. Trend analysis revealed significant long–term declines, most pronounced during summer and fall. Discharge is predominantly controlled by stable spring SWE, while discharge and temperature jointly influence periodic SWE in summer–fall, with warming reducing the delta surface water. Ephemeral SWE responds episodically to flow pulses, whereas precipitation played a limited role in this semi–arid region. Spatially, area(s) of interest (AOI)-II/III (the main distributary system) support the most extensive yet dynamic wetlands. In contrast, AOI-I and AOI-IV host smaller, more constrained wetland mosaics. AOI-I shows persistence under steady low flows, while AOI-IV reflects a stressed system with sporadic high-water levels. Overall, the results highlight the dominant influence of flow regulation and distributary allocation on IRD hydrology and the need for ecologically timed releases, targeted restoration, and transboundary cooperation to sustain delta resilience. Full article

Accurate precipitation estimation is essential for hydrological modeling and water resource management in arid regions; however, complex terrain and sparse meteorological station networks introduce substantial uncertainties into gridded precipitation datasets. This study evaluates the performance of nine widely used precipitation products in the arid region of Northwest China (ARNC) at both the meteorological station scale and the sub-basin scale, and applies the Bayesian Model Averaging (BMA) approach to merge multi-source precipitation estimates. The results reveal pronounced spatial heterogeneity and significant differences in performance among datasets, with the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement mission performing best at the station scale and the Famine Early Warning Systems Network Land Data Assimilation System performing best at the sub-basin scale. Compared with individual products, the BMA-merged precipitation demonstrates substantial improvements at both scales, providing higher coefficients of determination and agreement indices, and lower relative mean absolute error and relative root mean square error, indicating enhanced accuracy and robustness. The BMA-merged precipitation product generally exhibits superior and more spatially consistent performance than the individual datasets across the ARNC, thereby providing a more reliable basis for regional hydrological and climate-related applications. The merged dataset shows that the mean annual precipitation in the ARNC during 2000–2024 is approximately 230.4 mm, exhibiting a statistically significant increasing trend of 1.4 mm per year, with the strongest increases occurring in the Tianshan and Qilian Mountains. This study provides a reliable foundation for hydrological modeling and climate-change assessments in data-limited arid environments. Full article

Urban Water Systems (UWS) are complex infrastructures that interact with energy, food, ecosystems and socio-political systems, and are under growing pressure from climate change and resource depletion. Planning circular interventions in this context requires system-level analysis to avoid fragmented, siloed decisions. This paper develops the Hydrosocial Resource Urban Nexus (HRUN) framework that integrates hydrosocial thinking with the Water–Energy–Food–Ecosystems (WEFE) nexus to guide UWS design. We conduct a structured literature review and analyse different configurations of circular interventions, mapping their synergies and trade-offs across socioeconomic and environmental functions of hydrosocial systems. The framework is operationalised through a typology of circular interventions based on their circularity purpose (water reuse, resource recovery and reuse, or water-cycle restoration) and management scale (from on-site to centralised), while greening degree (from grey to green infrastructure) and digitalisation (integration of sensors and control systems) are treated as transversal strategies that shape their operational profile. Building on this typology, we construct cause–effect matrices for each intervention type, linking recurring operational patterns to hydrosocial functionalities and revealing associated synergies and trade-offs. Overall, the study advances understanding of how circular interventions with different configurations can strengthen or weaken system resilience and sustainability outcomes. The framework provides a basis for integrated planning and for quantitative and participatory tools that can assess trade-offs and governance effects of different circular design choices, thereby supporting the transition to more resilient and just water systems. Full article

Understanding wake interactions between multiple tidal stream turbines is essential for optimizing the performance and layout of tidal energy farms. This study investigates the hydrodynamic behavior of two horizontal-axis tidal turbines arranged in tandem under simplified inflow conditions, where the incoming flow was dominated by the streamwise velocity component without imposed external disturbances. Wake measurements were conducted in a large circulating water tunnel using a mobile, submerged particle image velocimetry (PIV) system capable of long-range, high-resolution measurements. Performance tests showed that the downstream turbine experienced a decrease of approximately 9% in maximum power coefficient compared to the upstream turbine due to reduced inflow velocity and increased turbulence generated by the upstream wake. Phase-averaged PIV results revealed the detailed evolution of velocity deficit, turbulence intensity, turbulent kinetic energy, and tip vortex structures. The tip vortices shed from the upstream turbine persisted over a long downstream distance, remaining coherent up to 10D and merging with those generated by the downstream turbine. These merged vortex structures produced elevated turbulence and complex flow patterns that significantly influenced the downstream turbine’s operating conditions. The results provide experimentally validated insight into turbine-to-turbine wake interactions and highlight the need for high-fidelity wake data to support array optimization and numerical model development for tidal stream turbine array. Full article

In the context of global climate change and intensified water resource constraints, studying the evolution of the urban–agricultural–ecological spatial structure and the water–heat–vegetation responses driven by large-scale irrigation and drainage projects in arid and semi-arid regions is of great significance. Based on multitemporal remote sensing data from 1985 to 2015, this study takes the Inner Mongolia Hetao Plain as the research area, constructs a “multifunctionality–dynamic evolution” dual-principle classification system for urban–agricultural–ecological space, and adopts the technical process of “separate interpretation of each single land type using the maximum likelihood algorithm followed by merging with conflict pixel resolution” to improve the classification accuracy to 90.82%. Through a land use transfer matrix, a standard deviation ellipse model, surface temperature (LST) inversion, and vegetation fractional coverage (VFC) analysis, this study systematically reveals the spatiotemporal differentiation patterns of spatial structure evolution and surface parameter responses throughout the project’s life cycle. The results show the following: (1) The spatial structure follows the path of “short-term intense disturbance–long-term stable optimization”, with agricultural space stability increasing by 4.8%, the ecological core area retention rate exceeding 90%, and urban space expanding with a shift from external encroachment to internal filling, realizing “stable grain yield with unchanged cultivated land area and improved ecological quality with controlled green space loss”. (2) The overall VFC shows a trend of “central area stable increase (annual growth rate 0.8%), eastern area fluctuating recovery (cyclic amplitude ±12%), and western area local improvement (key patches increased by 18%)”. (3) The LST-VFC relationship presents spatiotemporal misalignment, with a 0.8–1.2 °C anomalous cooling in the central region during the construction period (despite a 15% VFC decrease), driven by irrigation water thermal inertia, and a disrupted linear correlation after completion due to crop phenology changes and plastic film mulching. (4) Irrigation and drainage projects optimize water resource allocation, constructing a hub regulation model integrated with the Water–Energy–Food (WEF) Nexus, providing a replicable paradigm for ecological effect assessment of major water conservancy projects in arid regions. Full article

Europe is dealing with environmental problems that require cooperation beyond national and regional borders. Air pollution, water pollution, biodiversity loss, and waste management are the major issues that are not only complex but also cross borders. Therefore, it is necessary to provide collaborative responses that go beyond the capacity of individual countries. This inquiry centers on the question of what the best way is to set up and govern the transnational cooperation in Europe to confront these major environmental challenges. A systematic bibliographic review of the research conducted between 2010 and 2025 forms the basis of this work. The research combines semantic analysis and Latent Dirichlet Allocation (LDA) modeling to study 80 selected publications to find the tenets of the themes discussed. The identified topics are urban climate change adaptation and mitigation, climate policy and management, adaptation and vulnerability frameworks, land use and biodiversity impacts, and future climate projections and assessment. The findings show that there are strong synergies between biodiversity and climate adaptation, resilience, and environmental governance, as well as the great influence of climate change on the water management sector. The study has unveiled the significance of institutional policy frameworks in bringing about environmental cooperation across borders. In addition, it depicts the relationship between local urban projects and supra-regional policy strategies, in which the two can merge and function efficiently as long as they are working towards the common goal of environmental sustainability. This study is meant to shed more light on the area of environmental governance research, discovering areas that need more exploration, and providing some signposts on how to improve environmental involvement in Europe. Full article

Smoothed particle hydrodynamics (SPHs) is a Lagrangian meshless method with distinct strengths in managing unstable and complex interface behaviors. This study develops an integrated multiphase SPH framework by merging multiple algorithms and techniques to enhance stability and accuracy. The multiphase model is validated by several benchmark examples, including square droplet deformation, single bubble rising, and two bubbles rising. The selection of numerical parameters for multiphase simulations is also discussed. The validated model is then applied to simulate oil–water–gas bubbly flows. Interface behaviors, such as coalescence, fragmentation, deformation, etc., are reproduced, which helps to take into account multiphysics interactions in industrial processes. The rising processes of many oil droplets for oil–water separation are first simulated, showing the advantages and stability of the SPH model in dealing with complex interface behaviors. To fully explore the potential of the model, the model is further extended to the field of wax removal. The melting process of the wax layer due to heat conduction is simulated by coupling the thermodynamic model and the phase change model. Interesting behaviors such as wax layer cracking, droplet detachment, and thermally driven flow instabilities are captured, providing insights into wax deposition mitigation strategies. This study provides an effective numerical model for bubbly flows in petroleum engineering and lays a research foundation for extending the application of the SPH method in other engineering fields, such as multiphase reactor design and environmental fluid dynamics. Full article

Acid soil aluminum (Al) considerably reduces crop productivity. This study examined whether transformation of supramolecular assembly of root cell wall polysaccharides (CWPs) contributes to genotype-specific responses to Al stress in triticale. CWPs were extracted from apical and hairy root segments of two triticale genotypes, differing in Al tolerance. Water-extractable polysaccharides (WEPs) and those extracted with trans-1,2-cyclohexanediaminetetraacetic acid (CDTA) and sodium carbonate (Na₂CO₃) were analyzed using the multi-detection high-performance size-exclusion chromatography (HPSEC-RI-LALS/RALS-DV-UV-Vis). WEPs most clearly reflected differences between genotypes in macromolecular organization and Al-induced modification. Both root segments contained high molar mass (HM) subunits of WEPs with distinct anisotropic light scatterer (AS) domains. AS domains of a tolerant genotype were symmetrically elongated and branched, whereas those of a sensitive one were asymmetrically elongated with a spherical shape. In both genotypes, Al stress induced an association of apical HM subunits to higher molar mass forms, but in a different manner. The tolerant genotype maintained branched AS domain architecture by forming separate HM subunits that prevented Al infiltration. In contrast, the sensitive genotype showed complete merging of all HM subunits into a micro gel structure, leading to AS surface degradation. These findings provide novel insight into the role of root AS domains and supramolecular cell wall organization in plant adaptation to abiotic stress. Full article

This study develops a satellite-based, machine-learning-based prediction algorithm to predict optimal irrigation scheduling for cotton cultivation within Rahim Yar Khan, Pakistan. The framework leverages multispectral satellite imagery (Landsat 8 and Sentinel-2), GIS-derived climatic, land surface data and real-time weather information obtained from a freely accessible weather API, eliminating the need for ground-based IoT sensors. The proposed algorithm integrates FAO-56 evapotranspiration principles and water stress indices to accurately forecast irrigation requirements across the four critical growth stages of cotton. Supervised learning algorithms, including Gradient Boosting, Random Forest, and Logistic Regression, were evaluated, with Random Forest indicating better predictive accuracy with a coefficient of determination (R²) exceeding 0.92 and a root mean square error (RMSE) of approximately 415 kg/ha, owed its capacity to handle complex, non-linear relations, and feature interactions. The model was trained on data collected during 2023 and 2024, and its predictions for 2025 were validated against observed irrigation requirements. The proposed model enabled an average 12–18% reduction in total water application between 2023 and 2025, optimizing water use deprived of compromising crop yield. By merging satellite imagery, GIS data, and weather API information, this approach provides a cost-effective, scalable solution that enables precise, stage-specific irrigation scheduling. Cloud masking was executed by applying the built-in QA bands with the Fmask algorithm to eliminate cloud and cloud-shadow pixels in satellite imagery statistics. Time series were generated by compositing monthly median values to ensure consistency across images. The novelty of our study primarily focuses on its end-to-end integration framework, its application within semi-arid agronomic conditions, and its empirical validation and accuracy calculation over direct association of multi-source statistics with FAO-guided irrigation scheduling to support sustainable cotton cultivation. The quantification of irrigation capacity, determining how much water to apply, is identified as a focus for future research. Full article

Ancient underground voids present non-trivial hazards to urban redevelopment, particularly where groundwater conditions change during construction. We propose a staged, groundwater-aware workflow that integrates in-void mapping with area-scale geophysics and explicitly links water state to imaging performance. Following exposure of an undocumented masonry tunnel in a foundation pit in Wuhan (China), we acquired underwater CCTV and sonar during water-filled conditions, and, after drainage, collected ground-penetrating radar (GPR, 75–150 MHz) and ultra-high-density electrical resistivity tomography (UHD-ERT, 1 m electrode spacing) data. Calibration lines over the breach anchored the depth/geometry and reduced interpretational non-uniqueness. Analytical estimates using Archie-type and CRIM relations, together with observed signatures, indicate that drainage increased resistivity and reduced electromagnetic attenuation, improving UHD-ERT contrast and GPR penetration. The merged evidence resolves a straight-walled arch (~1.8 m wide × ~1.9 m high) at ~4–5 m depth with a sealed end 4 m south of the breach. Sonar confirms a northward segment measuring 45 ± 2 m to a sealed wall; a GPR void-type anomaly at ~57 m along trend represents a candidate continuation that remains unverified with current access. Within the resolution and sensitivity of the 2D survey, no additional voids were detected elsewhere on site. This case demonstrates that coupling in-void CCTV/sonar with post-drainage GPR and UHD-ERT, organized by hydrologic stage, yields engineering-grade constraints for risk control. The workflow and boundary conditions provide a transferable template for water-influenced, urban environments. Full article

Floods increasingly threaten semiarid regions, yet their long-term soil ecological impacts remain underdocumented. This study quantifies the hydrologic change and flood-induced soil transformation on the Zhabay River floodplain (Akmola, Kazakhstan) using integrated field, laboratory, and remote sensing data. Gauge records (2012–2024) were analyzed; inundation was mapped from a 0.30 m DEM (Digital Elevation Model) merging SRTM (Shuttle Radar Topography Mission), Landsat 8/Sentinel 2, and UAV (Unmanned Aerial Vehicle) photogrammetry (NDWI (Normalized Difference Water Index) > 0.28) and validated with 54 in situ depths (MAE (Mean Absolute Error) 0.17 m). Soil samples collected before and after floods were analyzed for texture, bulk density, pH, Eh, macronutrients, and heavy metals. Annual maxima increased by 0.08 m yr⁻¹, while extreme floods became more frequent. Thresholds of ≥0.5 m depth and >7 days duration marked compaction onset, whereas >1 m and ≥12 days produced maximum organic carbon loss and Zn/Ni enrichment. The combination of high-resolution DEMs, ROC (Receiver Operating Characteristic) analysis, and soil microbial monitoring provides new operational indicators of soil degradation for Central Asian steppe floodplains. Findings contribute to SDG 13 (Climate Action) and SDG 15 (Life on Land) by linking flood resilience assessment with sustainable land-use planning. Full article

This paper presents a novel pipeline leak diagnosis framework that combines Savitzky–Golay scalograms with a lightweight advanced deep learning architecture. Pipelines are critical for transporting fluids and gases, but leaks can lead to operational disruptions, environmental hazards, and financial losses. Leak events generate acoustic emissions (AE), captured as transient signals by AE sensors; however, these signals are often masked by noise and affected by the transported medium. To overcome this challenge, a fluid-independent detection approach is proposed that begins with acquiring AE data under various operational conditions, including multiple intensities of pinhole leaks and normal states. The transient signals are transformed into detailed scalograms using the Continuous Wavelet Transform (CWT), revealing subtle time–frequency patterns associated with leak events. To enhance these leak-specific features, a targeted Savitzky–Golay (SG) filter is applied, producing refined Savitzky–Golay scalograms (SG scalograms). These SG scalograms are then used to train a Convolutional Neural Network (CNN) and a newly developed lightweight Vision Transformer with streamlined self-attention (LViT-S), which autonomously learn both local and global features. The LViT-S achieves reduced embedding dimensions and fewer Transformer layers, significantly lowering computational cost while maintaining high performance. Extracted local and global features are merged into a unified feature vector, representing diverse visual characteristics learned by each network through their respective loss functions. This comprehensive feature representation is then passed to an Artificial Neural Network (ANN) for final classification, accurately identifying the presence, severity, and absence of leaks. The effectiveness of the proposed method is evaluated under two different pressure conditions, two fluid types (gas and water), and three distinct leak sizes, achieving a high classification accuracy of 98.6%. Additionally, a comparative evaluation against four state-of-the-art methods demonstrates that the proposed framework consistently delivers superior accuracy across diverse operational scenarios. Full article

This study explores the integration of point cloud data obtained from unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) to address limitations in photogrammetry and to create comprehensive models of aquatic environments. The UAV platform (AUTEL EVO II) employs structure-from-motion (SfM) photogrammetry using optical imagery, while the USV (equipped with a NORBIT iWBMS multibeam sonar system) collects underwater bathymetric data. UAVs commonly face constraints in battery life and image-processing capacity, making it necessary to merge smaller UAV point clouds into larger, more complete models. The USV-derived bathymetric data are integrated with UAV-derived surface data to construct unified terrain models that include both above-water and underwater features. This study evaluates three coordinate transformation (CT) methods—4-parameter, 6-parameter, and 7-parameter—across three study areas in Taiwan to assess their effectiveness in registering USV–UAV and UAV–UAV point clouds. For USV–UAV integration, all CT methods improved alignment accuracy compared with results without CT, achieving decimeter-level precision. For UAV–UAV integrations, the 7-parameter method provided the best accuracy, especially in areas with low terrain roughness such as rooftops and pavements, while improvements were less pronounced in areas with high roughness such as tree canopies. These findings demonstrate that the 7-parameter CT method offers an effective and straightforward approach for accurate point cloud integration from different platforms and sensors. Full article

The 2011 Great Flood in Thailand exposed critical deficiencies in water management across the Chao Phraya River Basin, particularly in controlling inflows and discharges from major reservoirs such as Sirikit and Bhumibol. Inadequate rainfall monitoring at the Nakhon Sawan station further intensified the disaster’s impact. As climate change continues to amplify extreme weather events, this study aims to improve flood forecasting accuracy and promote sustainable water resource management aligned with the Sustainable Development Goals (SDGs 6, 11, and 13). Advanced climate data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were spatially refined and integrated with hydrological models to enhance regional accuracy. The Discrete Wavelet Transform (DWT) was applied for feature extraction to capture hydrological variability, while the Nonlinear Autoregressive Model with Exogenous Factors (NARX) was employed to model complex temporal relationships. A multi-model ensemble framework was developed to merge climate forecasts with real-time hydrological data. Results demonstrate significant model performance improvements, with DWT-NARX achieving 55–98% lower prediction errors (RMSE) compared to baseline methods and correlation coefficients exceeding 0.91 across all forecasting scenarios. Marked seasonal variations emerge, with higher inflows during wet periods and reduced inflows during dry seasons. Under RCP8.5 climate scenarios, wet-season inflows are projected to increase by 15.8–17.4% by 2099, while dry-season flows may decline by up to 33.5%, potentially challenging future water availability and flood control operations. These findings highlight the need for adaptive and sustainable water management strategies to enhance climate resilience and advance SDG targets on water security, disaster risk reduction, and climate adaptation. Full article

Understanding global precipitation trends is critical for managing water resources, anticipating extreme events, and assessing the impacts of climate change. This study analyzes spatial and temporal patterns of precipitation from 1998 to 2024 using NASA’s Global Precipitation Measurement Mission (GPM) Integrated Multi-satellite Retrievals for (IMERG) Version 7, which merges satellite observations with rain-gauge data at 0.1° resolution. A total of 324 monthly datasets were aggregated into annual and seasonal composites to evaluate annual and seasonal trends in global precipitation. The non-parametric Mann–Kendall test was applied at the pixel scale to detect statistically significant monotonic trends, and Sen’s slope estimator method was used to quantify the magnitude of change in mean annual and seasonal global precipitation. Results reveal robust and geographically consistent patterns: significant wetting trends are evident in high-latitude regions, with the Arctic and Southern Oceans showing the strongest increases across multiple seasons, including +0.04 mm/day in December–January–February for the Arctic Ocean and +0.04 mm/day in June–July–August for the Southern Ocean. Northern China also demonstrates persistent increases, aligned with recent intensification of extreme late-season precipitation. In contrast, significant drying trends are detected in the tropical East Pacific (up to −0.02 mm/day), northern South America, and some areas in central-southern Africa, highlighting regions at risk of sustained hydroclimatic stress. The North Atlantic south of Greenland emerges as a summer drying hotspot, consistent with Greenland Ice Sheet melt enhancing stratification and reducing precipitation. Collectively, the findings underscore a dual pattern of wetting at high latitudes and drying in tropical belts, emphasizing the role of polar amplification, ocean–atmosphere interactions, and climate variability in shaping Earth’s precipitation dynamics. Full article