Search Results (3,621)

Assessment of crop water requirements (ETc) and their meteorological driving mechanisms are critical for irrigation management in arid inland river basins. Taking the Tailan River Irrigation District (Xinjiang, China) as a case study, temporal changes in cropping structure, crop-specific ETc, and irrigation-district–scale agricultural water demand, as well as the meteorological controls on ETc, were quantified for the period 2000–2024 using Google Earth Engine-based crop mapping, the CROPWAT model, and path analysis. The results demonstrated that the 2024 random forest classification model achieved high accuracy (overall accuracy = 0.902; Kappa = 0.876), and validation against statistical yearbook data confirmed the reliability of crop-area estimation. Cotton dominated the cropping structure (228.6–426.0 km²), while the orchard area expanded markedly from 206.5 km² in 2000 to 393.2 km² in 2024; wheat exhibited strong interannual variability, and maize occupied a relatively small area. Crop-specific ETc differed markedly among crop types, following the order orchard > cotton > maize > wheat, with orchards maintaining the highest water requirement across all growth stages. Total agricultural water demand, estimated by integrating crop-specific ETc with remotely sensed planting areas, increased from approximately 260 million m³ to over 500 million m³ after 2010, mainly due to orchard expansion and cotton cultivation. Path analysis indicated that interannual ETc variability exhibited a stronger statistical association with wind speed than with other meteorological variables. These results provide a quantitative basis for cropping-structure optimization and water-saving irrigation management under changing climatic conditions. Full article

Non-point source pollution poses a severe threat to the water quality of the Li River. This study conducted field monitoring of pollution loads from different land-use types on Maozhou Island in the Li River during the 2023 rainy season. Runoff water quality from vegetable plots, orchards, and bamboo forests consistently exceeded standards, with vegetable plots being the primary source of pollution. Their total phosphorus (TP) concentration exceeded standards by nearly 25 times, contributing the highest annual load. The transport of pollutants (TP, total nitrogen(TN), chemical oxygen demand(COD_Cr)) was closely correlated with suspended solids (SS), with the finest particles (<5 μm) identified as the primary carrier exhibiting the strongest pollutant enrichment capacity (e.g., in vegetable fields, the correlation coefficient r between < 5 μm particles and TP was >0.85, p < 0.01). Rainfall patterns significantly influenced pollutant concentrations; TN and TP levels increased with preceding dry days, while phosphorus output from vegetable plots decreased with rising average rainfall temperature. Compared to bamboo forests, vegetable plots and orchards exhibited lower soil adsorption capacity. This study recommends a connectivity-based strategy prioritizing the interception of heavily enriched fine particulate matter (<5 μm) through runoff control and enhanced wetland retention functions. These findings underscore the importance of controlling fine particulate matter for reducing non-point source pollution and maintaining ecological health in the Lijiang River basin. Full article

Impulsive overtopping generated by dam-break surges is a critical hazard for dikes and flood-protection embankments, especially in reservoirs and mountainous catchments. Unlike classical coastal wave overtopping, which is governed by long, irregular wave trains and usually characterized by mean overtopping discharge over many waves, these dam-break-type events are dominated by one or a few strongly nonlinear bores with highly transient overtopping heights. Accurately predicting the resulting overtopping levels under such impulsive flows is therefore important for flood-risk assessment and emergency planning. Conventional cluster-then-predict approaches, which have been proposed in recent years, often first partition data into subgroups and then train separate models for each cluster. However, these methods often suffer from rigid boundaries and ignore the uncertainty information contained in clustering results. To overcome these limitations, we propose a GMM+MoE framework that integrates Gaussian Mixture Model (GMM) soft clustering with a Mixture-of-Experts (MoE) predictor. GMM provides posterior probabilities of regime membership, which are used by the MoE gating mechanism to adaptively assign expert models. Using SPH-simulated overtopping data with physically interpretable dimensionless parameters, the framework is benchmarked against XGBoost, GMM+XGBoost, MoE, and Random Forest. Results show that GMM+MoE achieves the highest accuracy ($R^2=0.9638$ on the testing dataset) and the most centralized residual distribution, confirming its robustness. Furthermore, SHAP-based feature attribution reveals that relative propagation distance and wave height are the dominant drivers of overtopping, providing physically consistent explanations. This demonstrates that combining soft clustering with adaptive expert allocation not only improves accuracy but also enhances interpretability, offering a practical tool for dike safety assessment and flood-risk management in reservoirs and mountain river valleys. Full article

Zooplankton is an essential functional component of the aquatic food web, reflecting, through its structure and biomass, the impact of anthropogenic pressures on ecosystems. In this study, we investigated the traits of the Rotifera and Crustacea communities along a rural–urban gradient in the Colentina River system. The results revealed a partial separation between rotifers and crustaceans, with distinct distributions determined by trophic conditions and habitat type. Trophic indices (Carlson’s TSI, TSI_ROT, TSI_CR) indicated increased eutrophication in peri-urban and urban areas (Fundeni, Plumbuita) compared to rural reference ecosystems (Colentina, Crevedia). The relationships between Resource Use Efficiency (RUE) and trophic indices were positive and significant in rural areas, indicating a balanced ecosystem, but were decoupled in urbanised sectors, where high RUE values were driven by increased biomass of opportunistic species, whereas TSI indicated eutrophic conditions. The results confirm the role of zooplankton as a sensitive bioindicator, capable of capturing both the impact of eutrophication and the capacity of urbanised ecosystems to maintain trophic functionality. The integration of zooplankton-based metrics into monitoring schemes offers a complementary perspective on ecological resilience in aquatic ecosystems under urban pressures. Full article

This study is essential for medium- and long-term land-use management, as land-use patterns directly influence local economic and social development. Geographic Information System (GIS) techniques are fundamental tools for analyzing a wide range of geomorphological processes, including relief fragmentation density, relief energy, soil texture, slope gradient, and slope orientation. The present research focuses on the Pesceana river basin in the Southern Carpathians, Romania. It addresses three main objectives: (1) to analyze land-use dynamics derived from CORINE Land Cover (CLC) data between 1990 and 2018, along with the long-term distribution of the Normalized Difference Vegetation Index (NDVI) for the period 2000–2025; (2) to evaluate the basin’s natural potential byintegrating topographic data (contour lines and profiles) with relief fragmentation density, relief energy, vegetation cover, soil texture, slope gradient, aspect, the Stream Power Index (SPI), and the Topographic Wetness Index (TWI); and (3) to assess the spatial distribution of habitat types, characteristic plant associations, and soil properties obtained through field investigations. For the first two research objectives, ArcGIS v. 10.7.2 served as the main tool for geospatial processing. For the third, field data were essential for geolocating soil samples and defining vegetation types across the entire 247 km² area. The spatiotemporal analysis from 1990 to 2018 reveals a landscape in which deciduous forests clearly dominate; they expanded from an initial area of 80 km² in 1990 to over 90 km² in 2012–2018. This increase, together with agricultural expansion, is reflected in the NDVI values after 2000, which show a sharp increase in vegetation density. Interestingly, other categories—such as water bodies, natural grasslands, and industrial areas—barely changed, each consistently representing less than 1 km² throughout the study period. These findings emphasize the importance of land-use/land-cover (LULC) data within the applied GIS model, which enhances the spatial characterization of geomorphological processes—such as vegetation distribution, soil texture, slope morphology, and relief fragmentation density. This integration allows a realistic assessment of the physical–geographic, landscape, and pedological conditions of the river basin. Full article

Compound dry–hot events increasingly threaten ecosystem productivity under global warming. Using ERA5-Land and MODIS NPP (2002–2024) for the Yangtze River Basin, we built climate indices and developed a Copula-based standardized compound dry–hot index (SCDHI) to detect events and examine spatiotemporal patterns. Trend and correlation analyses quantified NPP sensitivity and lag, and an NPP–SCDHI coupling framework assessed resistance and resilience across major vegetation types. Basin-wide monthly NPP increased slightly, while SCDHI decreased, indicating a warmer and drier tendency. Under dry–hot conditions, NPP was mainly negatively related to event intensity in the upper basin but positively related across much of the middle–lower plains. The mean NPP response time was approximately 2 months, with forests and croplands typically lagging 2–3 months. Under extreme stress, forests showed high resistance but limited recovery, whereas shrublands showed moderate resistance and low resilience. Cultivated vegetation exhibited the lowest resistance and weak resilience, grasslands had low resistance but relatively rapid recovery, and alpine vegetation showed moderate resistance and the highest resilience. Cultivated vegetation and grasslands may therefore represent high-risk types for ecological management. Full article

Water scarcity and ecological degradation driven by the expansion of irrigated agriculture in arid regions urgently necessitate a rigorous assessment of the combined impacts of climate change and crop-structure adjustments on irrigation water requirements (IWR). Taking the Qarqan River Basin as a case study, this study establishes an integrated framework that incorporates remote sensing (Landsat/MODIS), the AquaCrop-OS crop model, and a CNN-LSTM deep learning architecture to simulate historical IWR (2000–2024) and project future trajectories under CMIP6 climate scenarios. The results indicate that: (1) from 2000 to 2024, fruit tree area expanded from 120.3 to 320.3 km², cotton stabilized at approximately 165.3 km² after peaking at 187.9 km² in 2014, wheat recovered to 113.1 km², and maize varied between 23.7 and 85.0 km², indicating that fruit trees have become the dominant crop type. (2) Over the same period, total basin-wide IWR increased by 91% (3.7 × 10⁸ to 7.1 × 10⁸ m³), with fruit trees accounting for 44–68% of this growth. Logarithmic mean Divisia index (LMDI) decomposition further shows that meteorological factors and human activities jointly drove the increase in IWR, with cultivated-area expansion and cropping-structure change contributing most, while improvements in agricultural water-use efficiency partially offset the rise. (3) Projections for 2025–2100 suggest stronger structural dominance of fruit trees and cotton; the growing share of water-intensive cash crops may further elevate irrigation pressure. Under SSP5-8.5, a 30% reduction in fruit tree area in the late century could save 4.3% of irrigation water (0.33 × 10⁸ m³). Overall, this study provides dynamic projections and decision support for adaptive regulation of agricultural water resources in arid regions. Full article

This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Floods are among the most frequent and devastating natural hazards, particularly in developing countries such as Myanmar, where monsoon-driven rainfall and inadequate flood-control infrastructure exacerbate disaster impacts. This study presents a satellite-driven and interpretable framework for high-resolution flood susceptibility and risk assessment by integrating multi-source remote sensing and geospatial data with ensemble machine-learning models—Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)—implemented on the Google Earth Engine (GEE) platform. Eleven satellite- and GIS-derived predictors were used, including the Digital Elevation Model (DEM), slope, curvature, precipitation frequency, the Normalized Difference Vegetation Index (NDVI), land-use type, and distance to rivers, to develop flood susceptibility models. The Jenks natural breaks method was applied to classify flood susceptibility into five categories across Myanmar. Both models achieved excellent predictive performance, with area under the receiver operating characteristic curve (AUC) values of 0.943 for XGBoost and 0.936 for LightGBM, effectively distinguishing flood-prone from non-prone areas. XGBoost estimated that 26.1% of Myanmar’s territory falls within medium- to high-susceptibility zones, while LightGBM yielded a similar estimate of 25.3%. High-susceptibility regions were concentrated in the Ayeyarwady Delta, Rakhine coastal plains, and the Yangon region. SHapley Additive exPlanations (SHAP) analysis identified precipitation frequency, NDVI, and DEM as dominant factors, highlighting the ability of satellite-observed environmental indicators to capture flood-relevant surface processes. To incorporate exposure, population density and nighttime-light intensity were integrated with the susceptibility results to construct a natural–social flood risk framework. This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Full article

This study systematically investigates the lithofacies, sedimentary microfacies, vertical evolution, and spatial distribution of the braided river delta–shallow lacustrine carbonate mixed sedimentary rocks of the Upper Ganchaigou Formation in the Huatugou Oilfield of the Qaidam Basin, China. This study integrates data from field outcrops, core observations, thin section petrography, laboratory analyses, and well-logging interpretations. Based on these datasets, the sedimentary characteristics are identified, and a comprehensive sedimentary model is constructed. The results reveal that the study area contains five clastic facies, three types of mixed sedimentary facies, and ten sedimentary microfacies. Two distinct modes of mixed sedimentation are recognized: component mixing and stratigraphic mixing. A full lacustrine transgression–regression cycle is formed by the two types of mixed sedimentation characteristics, which exhibit noticeable differences in vertical evolution. Component mixing, which occurs in a mixed environment of continuous clastic supply and carbonate precipitation during the transgression, is the primary characteristic of the VIII–X oil formation. The mixed strata that make up the VI–VII oil formation show rhythmic interbedding of carbonate and clastic rocks. During the lacustrine regression, it shows the alternating sedimentary environment regulated by frequent variations in lacustrine levels. The planar distribution is affected by both intensity of sediment from the west and the changes in lacustrine level. During the lacustrine transgression, it is dominated by littoral-shallow lacustrine mixed beach bar and mixed sedimentary delta. On the other hand, during the lacustrine regression, it is dominated by laterally amalgamated sand bodies in the braided-river delta front. Based on this, a mixed sedimentary evolution model controlled by the coupling of “source–lacustrine level” is established. It offers a guide for reconstructing the sedimentary environment in basins that are similar to it and reveals the evolution path of mixed sedimentation in the short-axis source area of arid saline lacustrine basins. Full article

Soil erosion in the hilly and gully region of the middle reaches of the Yellow River is severe, threatening regional ecological security and the water–sediment balance of the Yellow River. The area features fragmented topography and significant spatial heterogeneity in soil thickness, forming a unique binary “soil–rock” structural system. The soil in the study area is characterized by silt-based loess, and the underlying bedrock is an interbedded Jurassic-Cretaceous sandstone and sandy shale. It has strong weathering, well-developed fissures, and good permeability, rather than dense impermeable rock layers. However, the spatiotemporal differentiation mechanism of soil moisture in this system remains unclear. This study focuses on the typical hilly and gully region—the Geqiugou watershed. Through field investigations, soil thickness sampling, multi-scale soil moisture monitoring, and analysis of meteorological data, it systematically examines the cascade relationships among microtopography, soil–rock combinations, soil moisture, and meteorological drivers. The results show that: (1) Based on the field survey of 323 sampling points in the study area, it was found that soil samples with a thickness of less than 50 cm accounted for 85%, which constituted the main structure of soil thickness in the region. Macrotopographic units control the spatial differentiation of soil thickness, forming a complete thickness gradient from erosional units (e.g., Gully and Furrow) to depositional units (e.g., Gently sloped terrace). Based on this, five typical soil–rock combination types with soil thicknesses of 10 cm, 30 cm, 50 cm, 70 cm, and 90 cm were identified. (2) Soil–rock combination structures regulate the vertical distribution and seasonal dynamics of soil moisture. In thin-layer combinations, soil moisture is primarily retained within the shallow soil profile with higher dynamics, whereas in thick-layer combinations, under conditions of substantial rainfall, moisture can percolate deeply and become notably stored within the fractured bedrock, sometimes exceeding the moisture content in the overlying soil. (3) The response of soil moisture to precipitation is hierarchical: light rain events only affect the surface layer, whereas heavy rainfall can infiltrate to depths below 70 cm. Under intense rainfall, the soil–rock interface acts as a rapid infiltration pathway. (4) The influence of meteorological drivers on soil moisture exhibits vertical differentiation and is significantly modulated by soil–rock combination types. This study reveals the critical role of microtopography-controlled soil–rock combination structures in the spatiotemporal differentiation of soil moisture, providing a scientific basis for the precise implementation of soil and water conservation measures and ecological restoration in the region. Full article

Recognizing the evolution of landscape patterns in the Chishui River source region is essential for protecting ecosystems and sustainable growth in the Yangtze River Basin and other similar areas. However, knowledge of landscape pattern evolution within the primary channel zone remains insufficient. To address this gap, the current study used 2000–2020 land-use, geography, and socio-economic data, integrating landscape pattern indices, land-use transfer matrices, dynamic degree, the GeoDetector model, and the PLUS model. Results revealed that forest and cropland remained the prevailing land-use types throughout 2000–2020, comprising over 85% of the landscape. Grassland had the highest dynamic degree (1.58%), and landscape evolution during the study period was characterized by increased fragmentation, enhanced diversity, and stable dominance of major forms of land use. Anthropogenic influence on different landscape types followed the order: construction land > cropland > grassland > forest > water bodies. Land-use change in this region is a complex process governed by the interrelationships among various factors. Scenario-based predictions demonstrate pronounced variability in various land types. These findings provided a more comprehensive understanding of landscape patterns in karst river source regions, provided evidence-based support for regional planning, and offered guidance for ecological management of similar global river sources. Full article

This study analyzes artificial intelligence development and green economic efficiency across 31 Chinese provinces using 2019–2021 panel data. We apply the entropy weight TOPSIS method to measure AI development levels. The entropy weight TOPSIS method measures AI development levels, the DEA-BCC model assesses green economic efficiency, and their coordination types are identified. Findings reveal a significant negative correlation between AI development and green economic efficiency. We explain this complex relationship through three mechanisms: short-term polarization effects, technology conversion lags, and spatial spillovers. Spatial analysis shows AI development forms high-high agglomerations in the Yangtze River Delta and Shandong. Green economic efficiency shows high-high clustering in the Beijing-Tianjin-Hebei region and selected western provinces. Using a “two-system” coupling framework, we identify four provincial categories. The “double-high” type should function as growth poles. The “high-low” type requires improved technology conversion efficiency. The “low-high” type can leverage ecological advantages. The “double-low” type needs enhanced factor inputs. We propose three targeted policy recommendations: establishing digital-green synergy platforms, implementing inter-provincial AI resource collaboration mechanisms, and developing locally adapted action plans. Full article

The northern part of the Naomugeng Sag in the Erlian Basin shows favorable sandstone-type uranium mineralization in the lower member of the Saihan Formation. The sandstone thickness ranges from 39.67 to 140.36 m, with an average sand content ratio of 76.33%, indicating broad prospecting potential. This study focuses on samples from uranium ore holes and uranium-mineralized holes in the area, conducting grain-size analysis of uranium-bearing sandstones, heavy mineral assemblage analysis, and detrital zircon U-Pb dating to systematically investigate provenance characteristics. The results indicate that the uranium-bearing sandstones in the lower member of the Saihan Formation were primarily transported by rolling and suspension, characteristic of braided river channel deposits. The heavy mineral assemblage is dominated by zircon + limonite + garnet + ilmenite, suggesting that the sedimentary provenance is mainly composed of intermediate-acid magmatic rocks with minor metamorphic components. Detrital zircon U-Pb ages are mainly concentrated in the ranges of 294–217 Ma (Early Permian to Late Triassic), 146–112 Ma (Middle Jurassic to Early Cretaceous), 434–304 Ma (Late Carboniferous to Early Permian), and 495–445 Ma (Middle–Late Ordovician to Early Silurian). Combined with comparisons of the ages of surrounding rock masses, the provenance of the uranium-bearing sandstones is mainly derived from intermediate-acid granites of the Early Permian–Late Triassic and Middle Jurassic–Early Cretaceous periods in the southern part of the Sonid Uplift, with minor contributions from metamorphic and volcanic rock fragments. The average zircon uranium content is 520.53 ppm, with a Th/U ratio of 0.73, indicating that the provenance not only supplied detrital materials but also provided uranium-rich rock bodies that contributed essential metallogenic materials for uranium mineralization. This study offers critical insights for regional prospecting and exploration deployment. Full article

The goal of this scientific study is to clarify whether and how digital–intelligent integration contributes to urban carbon efficiency and to identify the conditions under which this contribution becomes nonlinear and policy-relevant. Focusing on 39 prefecture-level cities in the middle reaches of the Yellow River Basin during 2011–2022, we adopt an integrated measurement–modelling approach that combines efficiency evaluation, machine-learning interpretation, and dynamic–spatial validation. Specifically, we construct two super-efficiency DEA indicators: an undesirable-output SBM incorporating CO₂ emissions and a conventional super-efficiency CCR index. We then estimate nonlinear city-level relationships using XGBoost and interpret the marginal effects with SHAP, while panel vector autoregression (PVAR) and spatial diagnostics are employed to validate the dynamic responses and spatial dependence. The results show that digital–intelligent integration is positively associated with both carbon-related and conventional efficiency, but its marginal contribution is strongly conditioned by human capital, urbanisation, and environmental regulation, exhibiting threshold-type behaviour and diminishing returns at higher digitalisation levels. Green efficiency reacts more strongly to short-run shocks, whereas conventional efficiency follows a steadier improvement trajectory. Heterogeneity across urban agglomerations and evidence of spatial clustering further suggest that uniform policy packages are unlikely to perform well. These findings highlight the importance of sequencing and policy complementarity: investments in digital infrastructure should be coordinated with institutional and structural measures such as green finance, environmental standards, and industrial upgrading and place-based pilots can help scale effective digital applications toward China’s dual-carbon objectives. The proposed framework is transferable to other regions where the digital–climate nexus is central to smart and sustainable urban development. Full article

Galba truncatula acts as an intermediate host for several parasitic flukes of veterinary importance, but a targeted study on its spatial presence as well as the impact of environmental factors in Southeastern Europe has not been conducted. During the summer months of 2017 and 2018, a survey of 191 water bodies in 14 districts in Central, Southern and Western Bulgaria was conducted, with a focus on animal drinking fountains. Each site was assessed for snail presence and characterized by altitude, temperature, precipitation, shade and type of water body. Logistic regression modeling was used to identify the important factors related to the occurrence of snail species. The frequency of habitats found was 29.3%, with no differences observed between the studied districts (p > 0.05). Snails were present across a wide range of altitudes (78–1926 m), annual mean temperature (7.8–14.0 °C) and annual mean precipitation (523–796 mm). The high habitat frequencies were recorded in streams (60.0%) and on the banks on small rivers (50.0%). The presence of snails in the two studied types of fountains (without or with a concrete platform) was 24.1% and 17.2%, respectively, with no significant difference between them (p > 0.05). Regression analysis revealed temperature, shade, and type of water body as factors that could significantly influence the spatial presence of G. truncatula. The findings demonstrate the ecological adaptability of G. truncatula and highlight its presence in habitats with high potential for contact with domestic and wild ruminants. This information fills a regional knowledge gap and can support risk assessment and control measures for fluke-borne diseases in livestock and wildlife. Full article