Search Results (368)

The inhibitory effect of vegetation on soil wind erosion along grassland highways in semi-arid regions has not been fully elucidated. In this study, the dry vegetation near S105 provincial highway in the Sangendalai area of Xilingol League, Inner Mongolia was selected for a wind tunnel test, and the vegetation coverage and porosity during the test were determined by using image processing methods. On this basis, a porous medium model of dry vegetation was established, and the two-phase flow of wind and sand was numerically simulated. The results show that: (1) The numerical simulation results are in good agreement with the wind tunnel observations, confirming the feasibility of using CFD to simulate wind erosion affected by vegetation along grassland highways in semi-arid areas. (2) The aerodynamic roughness of the grassland surface increases nonlinearly with the increase of vegetation cover, and the increase of aerodynamic roughness is more obvious when the vegetation cover is more than 16% in the scope of this study. (3) Vegetation changed the typical jump-dominated wind–sand flow structure on the bare ground surface, showing a significant interception and attenuation effect of vegetation, which was manifested by the reduction of sand accumulation at the wind outlet and the increase of deposition within the vegetated area, thus effectively inhibiting the wind erosion process. The results of the study provide methodological references and a theoretical basis for the study of wind erosion along grassland highways in semi-arid regions and help to promote the sustainable development and ecological balance of grassland ecosystems in semi-arid regions. Full article

Urban heat dynamics are strongly influenced by the interaction between built structures, surface materials, and vegetation cover. This study investigates the relationship between land surface temperature (LST) and key urban morphological and structural parameters in a municipality of Thessaloniki, Greece. LST was retrieved from Landsat imagery using the NDVI-based emissivity method within Google Earth Engine (GEE). To characterize the urban form of the study area, a WorldView-2 summer image was classified to extract indices of surface roughness, built-up density, greenness density, building orientation and roof material type. Statistical analyses, including regression models and one-way ANOVA, were applied to assess the influence of these parameters on LST variability. Results reveal significant correlations between LST and both structural and vegetative factors, highlighting the cooling role of urban greenness and the amplifying effect of dense built-up areas and specific roof materials. The findings provide valuable insights into the spatial drivers of urban heat at a high-resolution scale, and offer practical guidance for planning strategies designed to lessen heat intensity in compact urban environments. Full article

Globally, the combined pollution of fine particulate matter (PM_2.5) and ground-level ozone (O₃) poses severe challenges to public health and sustainable urban development. Recent data indicate that the annual average PM_2.5 concentration in the vast majority of cities worldwide fails to meet World Health Organization safety standards, with air pollution causing millions of premature deaths annually. As a nature-based solution, the purification efficacy of vegetation remains poorly quantified due to unclear coupling mechanisms with local meteorological conditions. This study systematically reviewed and synthesized 229 empirical studies published between 2000 and 2025 from Web of Science and China National Knowledge Infrastructure (CNKI), aiming to clarify the quantitative relationships and regulatory mechanisms of plant–meteorological synergistic purification of PM_2.5–O₃. Following double-blind independent screening (κ = 0.85) and data extraction, a quantitative minimal feasible synthesis approach was adopted due to high data heterogeneity. The results indicated the following. (1) The median canopy purification efficiency of urban vegetation for PM_2.5 was 18.2% (IQR: 12.5–30.1%, n = 17), with a median dry deposition velocity (Vd–PM) of 0.05 cm s⁻¹ (0.02–30 cm s⁻¹, n = 15). The median dry deposition velocity (Vd–O₃) for O₃ was 0.55 cm s⁻¹ (0.12–1.82 cm s⁻¹, n = 8), with non-stomatal deposition contributing approximately 35%. (2) Meteorological factors exhibit nonlinear regulation: relative humidity (RH) > 70% significantly enhances PM_2.5 adsorption, wind speeds of 1.5–3.0 m s⁻¹ are optimal for PM_2.5 deposition, and temperatures > 30 °C generally inhibit plant uptake of both pollutants (n = 7). (3) Functional traits strongly correlate with purification efficacy: species with high leaf roughness (R² = 0.8), high stomatal conductance, and low BVOC emissions (e.g., Ginkgo biloba, Platycladus orientalis) exhibit optimal synergistic purification potential. Species with high BVOC emissions (Populus przewalskii, Eucalyptus robusta) can increase daily net O₃ pollution equivalents by up to 86 g and must be strictly avoided. Based on quantitative evidence, a green space planning decision matrix indexed by climate zone and pollution type was developed, specifying vegetation configuration patterns, functional group selection, and key design parameters (canopy closure, green belt width, etc.) for different scenarios. This study provides an actionable scientific basis for precision planning and climate-adaptive management of urban green infrastructure. Full article

Urbanization alters the hydrological and structural functioning of tropical urban streams, influencing organic matter transport and retention processes. This study investigated leaf litter retention dynamics in the Bindá Stream in central Amazonia. A six-month leaf release experiment (100 leaves per 12 trial; 1200 leaves total) was conducted alongside hydrological monitoring and floristic surveys of riparian vegetation (adult and regeneration strata). Leaf retention remained consistently low (<33%) across sampling periods. Generalized linear models indicated that flow velocity and discharge were the primary predictors of retention probability, with higher hydrodynamic intensity significantly reducing in-stream storage. Riparian vegetation exhibited moderate structural complexity (Shannon H′ = 1.80; Structural Complexity Index = 3.80), yet limited channel roughness and physical obstructions constrained retention efficiency. Anthropogenic debris locally increased retention, but represents a structurally altered retention mechanism. Hydrodynamic forcing, rather than precipitation totals alone, governed organic matter transport dynamics. Reduced retention capacity suggests limited buffering of downstream material export under high-flow conditions. Although direct water-quality or epidemiological indicators were not measured, findings align with ecohydrological frameworks linking structural simplification and flow flashiness to diminished ecosystem regulation. These results inform riparian restoration and urban stormwater management strategies aimed at enhancing ecosystem regulation and water-quality buffering in tropical cities. Full article

Turbulent structure and diffusion over different underlying surfaces are fundamental to understanding mass and momentum exchange in the atmospheric boundary layer. This study investigated these processes over six distinct surfaces—flat plate, sand, grass, small gravel, large gravel, and vegetation—through wind tunnel experiments combined with high-frequency velocity measurements. Quadrant analysis, Reynolds stress decomposition, and turbulence kinetic energy budget analysis were employed to elucidate the mechanisms driving variations in diffusion coefficients. The results reveal two distinct turbulence generation regimes: over rigid surfaces (flat plate, sand, gravel), turbulence is primarily generated by roughness elements, whereas over canopy surfaces (grass, vegetation), canopy-induced shear and wake dynamics dominate. Consequently, the vertical profiles of turbulent diffusion coefficients K_x and K_z exhibit markedly different patterns across surface types. For rigid surfaces, diffusion coefficients peak near the surface and decay monotonically with height. For canopy surfaces, diffusion coefficients reach their maximum at the canopy top, reflecting the dual influence of canopy-induced shear and energy dissipation within the canopy. These findings provide a mechanistic understanding of surface-induced variability in turbulent diffusion processes and offer quantitative parameterizations that can improve pollutant dispersion modeling over complex terrain. Full article

Unmanned Aerial Vehicle (UAV) imagery has become an important tool for erosion monitoring, but little is known about its application in Mediterranean agricultural systems such as vineyards and olive groves. In this study, drone flights were conducted in vineyards and olive groves where mulch and biochar treatments had been applied. Digital terrain models (DTMs) and orthomosaics were constructed using a photogrammetry workflow, and model error was determined via global positioning system (GPS) transects. Erosion was assessed using Digital elevation models of Difference (DoD) and compared with field-based erosion plot measurements. Explanatory variables for erosion (soil roughness, slope length, steepness, vegetation cover) were derived from DTMs and orthomosaics and were evaluated in a multiple linear regression model. Although direct measurement of erosion from the DoDs was difficult, this was primarily influenced by the unexpectedly low erosion rates during the study period, and the high root mean square error (RMSE) of the DTMs. Significant differences in DTM-derived variables were found between study areas, and especially between areas with organic and integrated management, even though treatments showed similar patterns. The multiple linear regression model demonstrated strong explanatory power, accounting for a large part of the variation in measured erosion using the UAV-derived variables (R² = 0.81). Slope and slope length were the most important predictors of erosion together with the interaction between these two variables. The results suggest that soil erosion in the study areas was mostly determined by topographic and management factors, rather than the applied treatments. This study highlights the value of UAV imagery in advancing the understanding of erosion processes in Mediterranean agricultural systems, while also identifying the challenge of accurately measuring erosion from DoDs under conditions of low erosion rates. Full article

Mitigating heat stress in high-density historical districts remains a critical challenge in urban renewal due to complex morphological heterogeneity. Existing research often relies on isolated intervention measures, lacking systematic, multi-strategy assessments driven by high-precision spatial data. This study addresses this gap by establishing a quantitative framework that couples thermal infrared remote sensing with Computational Fluid Dynamics (CFD) to optimize microclimate responses in Beijing’s Liulichang Historic District. Remote sensing data were utilized to retrieve high-resolution Land Surface Temperature (LST), providing accurate thermal boundary conditions for micro-scale wind-thermal simulations. A baseline scenario (S0) and seven renewal strategies (S1–S7)—integrating varying configurations of greenery, water bodies, and permeable pavements—were evaluated using pedestrian-level comfort indices. Results reveal that single-factor interventions yield marginal improvements or thermodynamic trade-offs; specifically, adding greenery (S1) in narrow street canyons increased aerodynamic roughness, thereby obstructing ventilation and inducing localized warming. Conversely, composite strategies significantly enhanced microclimatic quality. The “greenery-water-permeable pavement” strategy (S4) achieved optimal synergistic effects, characterized by substantial cooling and spatial homogenization. Regression analysis identified water bodies as the dominant cooling driver, where a 10% increase in water coverage resulted in a temperature reduction of approximately 5.17 °C. Conversely, greenery alone showed no statistically significant cooling contribution (p > 0.05) without the synergistic presence of water or pavement modifications. This research suggests that urban renewal in high-temperature zones (>36 °C) should prioritize composite cooling networks. Furthermore, vegetation layouts near wind corridors must be precisely regulated to prevent ventilation degradation. These findings provide a scientific basis for the climate-adaptive sustainable regeneration of culturally significant, high-density urban blocks. Full article

Roadside green spaces function as critical ecological barriers in urban environments, and their plant communities play a key role in improving regional air quality. This study investigates typical roadside plant communities in southern Xinjiang, a region characterized by extreme aridity and frequent dust storms. By quantifying indicators such as dust retention capacity at both individual and community levels, together with leaf surface microstructural characteristics, we evaluate the comprehensive dust retention performance of different community configuration patterns. The results show that: (1) Among the studied species, Juniperus chinensis ‘Kaizuca’ exhibited the highest dust retention capacity per unit leaf area, followed by Juniperus chinensis L. and Rosa rugosa Thunb. Among trees, Platanus acerifolia (Aiton) Willd showed the greatest dust retention capacity per individual plant; among shrubs, Rosa rugosa Thunb. performed strongly, and among herbaceous species, Lolium perenne L. exhibited relatively high dust retention capacity. (2) Leaf dust retention is governed by the synergistic effects of multiple traits, including leaf aspect ratio, stomatal aspect ratio, stomatal protrusion, stomatal density, wax layer characteristics, and surface roughness. Leaf aspect ratio exerts a significant positive direct effect on dust retention, whereas stomatal aspect ratio shows a significant negative direct effect. (3) At the community level, the multi-layered tree–shrub–herbaceous configuration dominated by Platanus acerifolia (Aiton) Willd exhibited the strongest dust retention capacity, making it the most effective configuration for roadside green spaces. Overall, this study provides a robust theoretical framework and empirical evidence for the scientific selection and optimized configuration of roadside vegetation in arid regions, thereby supporting the sustainable improvement of urban roadside air quality in southern Xinjiang. Full article

Soil erosion poses a significant threat to slope stability and ecological functionality. The litter layer, with its complex physical structure, enhances surface roughness, mitigates direct rainfall impact, and improves rainwater interception and soil retention. A litter of three typical slope-protection plant species from Wuhan, Hubei Province, China (Cynodon dactylon, Indigofera amblyantha, and Cinnamomum camphora) was selected for this experiment. This study quantified the effects of litter mulch at four coverage levels (0, 500, 800, and 1000 g/m² based on dry mass) on slope runoff and sediment yield under simulated rainfall conditions at an intensity of 60 mm/h for a duration of one hour. The results indicated that (1) all litter types and coverage amounts effectively delayed the initiation of slope runoff, though their efficiencies in runoff and sediment reduction varied significantly. (2) Compared with the bare slope, the sediment yield in the plots covered with litter from Cynodon dactylon, Cinnamomum camphora, and Indigofera amblyantha decreased by 96.5%, 67.5%, and 9.4%, respectively, at a coverage of 800 g/m². Runoff yield decreased by 56.9% and 29.7% in the plots covered with Cynodon dactylon and Cinnamomum camphora litter, whereas Indigofera amblyantha litter cover instead increased runoff yield by 31.6%. (3) Furthermore, increasing litter coverage from 500 to 1000 g/m² progressively reduced runoff by 29% to 84% and sediment yield by 27.3% to 93.6% compared to the bare slope. These findings demonstrate the importance of litter cover in reducing runoff and soil erosion, offering quantitative support for optimizing vegetation-based slope management. Full article

The development of passive microwave sensors traces back to Robert Dicke’s pioneering experiments in the 1940s. Since then, microwave radiometry has evolved into a key tool for Earth observation, strengthened by data from multiple satellite missions operating across different wavelengths. This paper reviews the state of the art in microwave radiometry for monitoring land surfaces. After introducing the theoretical foundations underpinning current missions, we present an overview of major satellite instruments. We then examine early theoretical advances in retrieving soil moisture and snow properties, two applications that contributed to the future development of satellite microwave radiometry missions for the observation of surface variables. Particular attention is given to radiative transfer theory and its solutions, which model the effects of roughness, vegetation, and snow cover. These approaches form the basis of today’s retrieval algorithms and remain central to future missions. Subsequent sections highlight the use of passive microwave data for estimating a variety of surface variables, the role of passive microwave in data assimilation systems and forthcoming missions dedicated to land monitoring. The review concludes with key achievements, ongoing challenges, and open issues—such as soil moisture retrieval under dense vegetation or snow property retrieval in melting conditions. Addressing these limitations is critical to fully exploiting microwave radiometry in the context of climate research and mitigation strategies. Full article

Numerical weather prediction (NWP) models are essential for precipitation forecasting but are constrained by coarse spatial resolutions (10–50 km), which fail to capture fine-scale variations required for regional disaster prevention, particularly in complex terrain. While statistical and machine learning downscaling methods have been developed to bridge this resolution gap, they predominantly operate as “black boxes” without explicit physical guidance, leading to predictions that violate meteorological principles and systematic underestimation of extreme precipitation events. To address these limitations, this study aims to develop a Physics-Informed Machine Learning framework that explicitly integrates multi-scale topographic modulation and physical consistency constraints into precipitation downscaling. Specifically, a Random Forest model enhanced with Multi-Scale Structural Similarity (MS-SSIM) loss and Physical Constraint Enhancement (MSSSIM-PCE-RF) was constructed. The model introduces elevation gradient weights at low-resolution layers and micro-topographic parameters (slope, surface roughness) at high-resolution layers, while enforcing physical consistency between precipitation intensity, radar reflectivity, and ground observations via the Z-R relationship. Based on hourly data from 2252 meteorological stations in Jiangxi Province (2021–2022), coupled with topographic factors (DEM, slope, aspect) and Normalized Difference Vegetation Index (NDVI), a technical framework of “data fusion–feature synergy–machine learning–spatial reconstruction” was established. Results demonstrate that the MSSSIM-PCE-RF model achieves a validation R² of 0.9465 and RMSE of 0.1865 mm, significantly outperforming the conventional RF model (R² = 0.9272). Notably, errors in high-altitude, steep-slope, and high-vegetation areas are reduced by 45.3%, 42.0%, and 43.1%, respectively, with peak precipitation period errors decreasing by 37.2%. Multi-scale topographic analysis reveals significant orographic lifting effects at 250–1000 m elevations, peak precipitation at 12–15° slopes, and abundant precipitation on south/southeast aspects. By explicitly embedding topographic modulation and physical consistency constraints, the model effectively alleviates systematic underestimation of extreme precipitation in complex terrain, providing high-resolution data support for transmission line disaster prevention and micro-meteorological risk assessment. Full article

Soil moisture is an essential climate variable exhibiting strong spatio-temporal dynamics, especially in the topsoil. Therefore, it is assessed multiple times by sensors within in situ networks, satellites, and by modeling of the Earth system. The resulting soil moisture fields from all methods are individual and non-congruent due to the imperfection of the methods and retrievals. But their spatial patterns have valuable similarities that call for investigation to foster intercomparison or even fusion of soil moisture products. In this research study, the similarity of spatial soil moisture patterns between passive microwave remote sensing products and Earth system modeling is investigated. We configure and apply spatial similarity metrics to enable a spatial comparison of the operational SMAP Dual Channel Algorithm (DCA) radiometer soil moisture product with the soil moisture output from IFS model runs of the ECMWF. The pattern assessment spans over the whole of Europe and aims to find the drivers behind the spatial soil moisture distributions at scales ranging from single grid cells (minimum) to continental (maximum) spatial scales, and between growing periods of wet (2021) and dry (2022) years. The two specifically configured metrics, total disagreement and mean category distance, showcase the opportunities and challenges when assessing spatial similarity in soil moisture fields across different scales. In addition, the potential drivers of the spatial moisture patterns were screened. Here, soil texture is the most influential single driver of spatial patterns in the IFS soil moisture runs, when analyzed in absolute terms [m³ m⁻³]. In relative terms of soil moisture [-] (soil wetness index), precipitation and soil temperature explain most of the variability of the IFS soil moisture for Europe. The SMAP retrievals are predominantly driven by the brightness temperatures, mostly influenced by surface temperature, vegetation water content, and soil roughness. These differences in drivers, as well as in methodology, culminate in an inherent discrepancy between the two soil moisture products. However, the assessment of their spatial patterns reveals the underlying similarity from the local to the continental scale. Full article

As urbanization increasingly compresses ecological spaces, traditional urban greening faces dual challenges of high maintenance costs and diminished ecological functions. Within this context, urban walls—characterized by their widespread distribution, diverse microhabitats, and relatively low levels of human intervention—are gaining recognition as valuable components of urban green infrastructure. Spontaneous wall vegetation, with its strong local adaptability and ecological functions, aligns well with emerging concepts of low-intervention, nature-based urban restoration. This study investigates the composition and environmental drivers of spontaneous wall plant communities across 321 plots on 100 urban walls in central Nanjing, China. Standardized vegetation surveys recorded species composition, cover, and wall-related environmental variables. Variance partitioning, canonical correspondence analysis, and multiple linear regression were applied to elucidate the relationships between plant diversity patterns and environmental factors. Results revealed high species diversity on urban walls, with 163 vascular plant species across 125 genera and 60 families. Retaining walls and spring plots exhibited more complex community structures. Environmental factors collectively explained 58.1% of the variation in plant communities, with wall inherent attributes contributing 23.1%. Diversity indices indicated a moderate level of richness and evenness, with an average Shannon index of 1.3 (0.6–2.5), Simpson index of 0.6 (0.02–0.9), and Patrick index of 1.9 (0.3–3.8). Microstructural attributes such as joint degradation and surface roughness facilitated colonization, highlighting the critical role of microhabitat heterogeneity in community assembly. As one of the first systematic studies on spontaneous vegetation of urban vertical structures in the Yangtze River Delta, this research provides foundational data on urban wall biodiversity and offers valuable insights for integrating native species into green infrastructure planning. Full article

Urbanization in mountainous regions alters hydrologic systems, yet the spatial patterning of residential (RA) and non-residential (NRA) areas in response to hydrologic constraints remains poorly quantified. In this study, we analyzed how such constraints shaped the distinct locational logic of RA and NRA expansion in the mountainous Kunming Core Region (KCR), Southwest China, from 1975 to 2020. Using the Global Human Settlement Layer (GHS-BUILT-S) built-up fraction data and its functionally classified RA and NRA layers at 100 m resolution, we quantified multi-decadal urban land changes via regression and centroid migration analyses. Six hydrologic factors, namely altitude, slope, surface roughness, distance to river (DTR), depth to water table (DTWT) and vegetation root depth (VRD), were derived from global terrain, groundwater, and rooting depth datasets, and harmonized to a common grid. Results show a two-phase urbanization pattern: moderate, compact growth before 1995 followed by rapid, near-exponential expansion, dominated by RA. RA consistently clustered in hydrologically favorable zones (low–moderate roughness, mid-altitudes, lower slopes, proximal rivers, shallow–moderate DTWT, moderate VRD), whereas NRA expanded into more hydrologically variable terrain (higher roughness, intermediate DTR, deeper DTWT, higher altitudes, deeper VRD). Contribution-weighting analysis revealed a temporal shift in dominant drivers: for RA, from river proximity and slope in 1975 to terrain roughness in 2020; for NRA, from vegetation root depth and moderate topography to root depth plus altitude. Geographic centroids of both RA and NRA migrated northeastward, indicating coordinated yet functionally distinct peri-urban and corridor-oriented growth. These findings provide a hierarchical, factor-based framework for integrating hydrologic constraints into risk-informed land-use planning in topographically complex basins. Full article

Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide reliable global SM and VOD products, they are limited by coarse spatial resolution and infrequent revisit times. Global Navigation Satellite System Reflectometry (GNSS-R) observations, particularly from the Cyclone GNSS (CYGNSS) mission, offer an improved spatiotemporal sampling rate. This study presents a deep learning framework based on an artificial neural network (ANN) for the simultaneous retrieval of SM and VOD from CYGNSS observations across the contiguous United States (CONUS). Ancillary input features, including specular point latitude and longitude (for spatial context), CYGNSS reflectivity and incidence angle (for surface signal characterization), total precipitation and soil temperature (for hydrological context), and soil clay content and surface roughness (for soil properties), are used to improve the estimates. Results demonstrate strong agreement between the predicted and reference values (SMAP SM and SMOS VOD), achieving correlation coefficients of R = 0.83 and 0.89 and RMSE values of 0.063 m³/m³ and 0.088 for SM and VOD, respectively. Temporal analyses show that the ANN accurately reproduces both seasonal and daily variations in SMAP SM and SMOS VOD (R ≈ 0.89). Moreover, the predicted SM and VOD maps show strong agreement with the reference SM and VOD maps (R ≈ 0.93). Additionally, ANN-derived VOD demonstrates strong consistency with above-ground biomass (R ≈ 0.77), canopy height (R ≈ 0.95), leaf area index (R = 96), and vegetation water content (R ≈ 0.90). These results demonstrate the generalizability of the approach and its applicability to broader environmental sensing tasks. Full article