Understanding changes in water balance and land-atmosphere interaction under climate change is crucial for managing water resources in alpine regions, especially in the Qinghai-Tibet Plateau (QTP). Evapotranspiration (ET), a key process in the land-atmosphere interaction, is influenced by permafrost degradation. As the active layer in permafrost regions deepens due to climate warming, the resulting shifts in surface hydrologic connectivity and water storage capacity affect vegetation's ability to access water, thereby influencing its growth and regulating ET dynamics, though the full complexity of this process remains unclear. This study employs the Budyko-Fu model to assess the spatiotemporal dynamics of ET and the ET ratio (the ratio of ET to precipitation) on the QTP from 1980 to 2100. While ET shows a continuous upward trend, the ET ratio exhibits a non-monotonic pattern, increasing initially and then decreasing. More than two-thirds of permafrost areas on the QTP surpassed the critical ET ratio threshold by 2023, under three emission scenarios. By 2100, nearly all areas are projected to reach the tipping point, with 97 % affected under the SSP5-8.5 scenario. Meadow and steppe regions are expected to encounter this threshold earlier, whereas forested areas will be less affected, with over 80 % unlikely to reach the tipping point by 2100. Basin-level differences are notable: nearly 90 % of the Qaidam basin exceeded the threshold before 2023, compared to less than 50 % in the Yangtze basin. By 2100, more than 80 % of regions in all basins are expected to cross the tipping point due to ongoing permafrost degradation. This study advances understanding of land-atmosphere interactions in alpine regions, providing critical insights for water resource management and improving extreme weather predictions.

Vegetation greening across the Tibetan Plateau, a critical ecological response to climate warming and land-cover change, affects soil hydrothermal regimes, altering soil moisture (SM) and soil temperature (ST) dynamics. However, its effects on SM-ST coupling remain poorly understood. Using integrated field measurements from a vegetation-soil (V-S) network, reanalysis, and physics-based simulations, we quantify responses of SM, ST, and their coupling to vegetation changes across the Upper Brahmaputra (UB) basin, southern Tibetan Plateau. Results show that strong positive SM-ST correlations occur throughout 0-289 cm soil layers across the basin, consistent with the monsoon-driven co-occurrence of rainy and warm seasons. Spatially, SM-ST coupling strength exhibits pronounced spatial heterogeneity, demonstrating strongest coupling in central basin areas with weaker intensities in eastern and western regions. Overall, vegetation greening consistently induces soil warming and drying: as leaf area index (LAI) increases from 20 % to 180 % of its natural levels, SM (0-160 cm) declines by 15 % to 29 % due to enhanced evapotranspiration and root water uptake. Mean ST simultaneously increases by 1.4 +/- 0.9 degrees C. Crucially, sparsely vegetated regions sustain warming (1.4-2.1 degrees C), while densely vegetated areas transition from initial warming to gradual cooling. These findings advance our understanding of soil hydrothermal dynamics and their broader environmental impacts, improving climate model parameterizations and informing sustainable land management strategies in high-altitude ecosystems.

Soil erosion can be effectively controlled through vegetation restoration. Specifically, roots combine with soil to form a root-soil complex, which can effectively enhance soil shear strength and play a crucial role in soil reinforcement. However, the relationship between root mechanical traits and chemical compositions and shear performance and reinforcing capacity of soil is still inadequate. In this study, we determined the root chemical properties, performed root tensile tests and root-soil composite triaxial tests using two plants-one with a fibrous root system (ryegrass, Lolium perenne L.) and the other with a tap root system (alfalfa, Medicago sativa L.)-and calculated the factor of safety (FOS). The results revealed that the relationship between root diameter and tensile strength differed among different root characters. Holocellulose content and cellulose content were the main factors controlling the root tensile strength of ryegrass and alfalfa, respectively. The shear properties of the root-soil complex (cohesion (c) and internal friction angle (phi)) are correlated with soil water content (SWC) and root mass density (RMD). Root traits had a more substantial effect on c than phi, with significant differences in c between ryegrass and alfalfa at 7 % and 11 % SWC. The root-soil complex had an optimum RMD, and the maximum increase rates of c were 80.57 % and 34.4 %, respectively. Along slopes, sliding first occurs at the foot of the slope, thus demanding emphasis on protection and reinforcement. On steep gradients with low SWC, ryegrass strongly contributes to soil reinforcement, whereas alfalfa is more effective on gentle gradients with high SWC. The results provide scientific references for species selection for vegetation restoration in the Loess Plateau and a deeper understanding of the mechanical mechanism of soil reinforcement by roots.

Forests are increasingly impacted by climate change, affecting tree growth and carbon sequestration. Tree-ring width, closely related to tree growth, is a key climate proxy, yet models describing ring width or growth often lack comprehensive environmental data. This study assesses ERA5-Land data for tree-ring width prediction compared to automatic weather station observations, emphasizing the value of extended and global climate data. We analyzed 723 site-averaged and detrended tree-ring chronologies from two broadleaved and two gymnosperm species across Europe, integrating them with ERA5-Land climate data, CO2 concentration, and a drought index (SPEI12). A subset was compared with weather station data. For modelling interannual variations of tree-ring width we used linear models to assess parameter importance. ERA5-Land and weather-station-based models performed similarly, maintaining stable correlations and consistent errors. Models based on meteorological data from weather stations highlighted SPEI12, sunshine duration, and temperature extremes, while ERA5-Land models emphasized SPEI12, dew-point temperature (humidity), and total precipitation. CO2 positively influenced the growth of gymnosperm species. ERA5-Land facilitated broader spatial analysis and incorporated additional factors like evaporation, snow cover, and soil moisture. Monthly assessments revealed the importance of parameters for each species. Our findings confirm that ERA5-Land is a reliable alternative for modeling tree growth, offering new insights into climate-vegetation interactions. The ready availability of underutilized parameters, such as air humidity, soil moisture and temperature, and runoff, enables their inclusion in future growth models. Using ERA5-Land can therefore deepen our understanding of forest responses to diverse environmental drivers on a global scale.

Understanding the relationship between soil moisture and vegetation is crucial for future projections of ecosystem and water resources. While their hysteresis loop relationship, which arises from their asynchrony in intra-annual variation, remains underexplored. This study used the hysteresis loop type and area (Ah) to characterize the relationship between root zone soil moisture (RZSM) and normalized difference vegetation index (NDVI) across China from 1986 to 2015, and examined its ecological implications. The results identified four types of hysteresis loops. The clockwise loop, with a delayed single peak of RZSM relative to NDVI, was primarily found in north China and the Qinghai-Tibet Plateau, indicating severe water limitation during early growth period. The counterclockwise loop, with an advanced single peak of RZSM relative to NDVI, was common in southeast China's forest, suggesting a shift towards energy limitation. The 8-shaped loop, resulting from double peaks in either RZSM or NDVI due to climate change (e.g., snowmelt) and human disturbance (e.g., irrigation and crop harvest), was observed in northwest China's glaciers and croplands in south and northeast China. The multicrossed loop, marked by multimodal intra-annual variations in both RZSM and NDVI, was predominantly found in northwest China's barren lands. Additionally, from 1986 to 2015, this study observed a shift from 8-shaped or multi-crossed loops to clockwise or counterclockwise loops in some regions like the Yellow River Basin, implying a trend of revegetation. Furthermore, a higher Ah generally indicated more severe water limitation or greater mismatch between RZSM and NDVI. Significant changes in Ah, such as increases in the Yellow River Basin, suggested intensified water limitations, while decreases in southeast and northwest China pointed to an earlier peak of the growing and rainy seasons. This study provides insights into the dynamic interactions between soil moisture and vegetation, offering valuable guidance for ecological management across diverse ecosystems.

Soil-plant-atmosphere interaction (SPAI) plays a significant role on the safety and serviceably of geotechnical infrastructure. The mechanical and hydraulic soil behaviour varies with the soil water content and pore water pressures (PWP), which are in turn affected by vegetation and weather conditions. Focusing on the hydraulic reinforcement that extraction of water through the plant roots offers, this study couples advances in ecohydrological modelling with advances in geotechnical modelling, overcoming previous crude assumptions around the application of climatic effects on the geotechnical analysis. A methodology for incorporating realistic ecohydrological effects in the geotechnical analysis is developed and validated, and applied in the case study of a cut slope in Newbury, UK, for which field monitoring data is available, to demonstrate its successful applicability in boundary value problems. The results demonstrate the positive effect of vegetation on the infrastructure by increasing the Factor of Safety. Finally, the effect of climate change and changes in slope vegetation cover are investigated. The analysis results demonstrate that slope behaviour depends on complex interactions between the climate and the soil hydraulic properties and cannot be solely anticipated based on climate data, but suctions and changes in suction need necessarily to be considered.

With polar amplification warming the northern high latitudes at an unprecedented rate, understanding the future dynamics of vegetation and the associated carbon-nitrogen cycle is increasingly critical. This study uses the dynamic vegetation model LPJ-GUESS 4.1 to simulate vegetation changes for a future climate scenario, generated by the EC-Earth3.3.1 Earth System model, with the forcing of a 560 ppm CO2 level. Using climate output from an earth system model without coupled dynamic vegetation, to run a higher resolution dynamic vegetation standalone model, allows for a more in depth exploration of vegetation changes. Plus, with this approach, the drivers of high latitude vegetation changes are isolated, but there is still a complete understanding of the climate system and the feedback mechanisms that contributed to it. Our simulations reveal an uneven greening response. The already vegetated Southern Scandinavia and western Russia undergo a shift in species composition as boreal species decline and temperate species expand. This is accompanied by a shift to a carbon sink, despite higher litterfall, root turnover and soil respiration rates, suggesting productivity increases are outpacing decomposition. The previously barren or marginal landscapes of Siberia and interior Alaska/Western Canada, undergo significant vegetation expansion, transitioning towards more stable, forested systems with enhanced carbon uptake. Yet, in the previously sparsely vegetated northern Scandinavia, under elevated CO2 temperate species quickly establish, bypassing the expected boreal progression due to surpassed climate thresholds. Here, despite rising productivity, there is a shift to a carbon source. The deeply frozen soils in central Siberia resist colonisation, underscoring the role of continuous permafrost in buffering ecological change. Together, these results highlight that CO2 induced greening does not always equate to enhanced carbon sequestration. The interplay of warming, nutrient constraints, permafrost dynamics and disturbance regimes creates divergent ecosystem trajectories across the northern high latitudes. These findings illustrate a strong need for regional differentiation in climate projections and carbon budget assessments, as the Arctic's role as a carbon sink may be more heterogeneous and vulnerable than previously assumed.

Land surface temperature (LST) plays an important role in Earth energy balance and water/carbon cycle processes and is recognized as an Essential Climate Variable (ECV) and an Essential Agricultural Variable (EAV). LST products that are issued from satellite observations mostly depict landscape-scale temperature due to their generally large footprint. This means that a pixel-based temperature integrates over various components, whereas temperature individual components are better suited for the purpose of evapotranspiration estimation, crop growth assessment, drought monitoring, etc. Thus, disentangling soil and vegetation temperatures is a real matter of concern. Moreover, most satellite-based LSTs are contaminated by directional effects due to the inherent anisotropy properties of most terrestrial targets. The characteristics of directional effects are closely linked to the properties of the target and controlled by the view and solar geometry. A singular angular signature is obtained in the hotspot geometry, i.e., when the sun, the satellite and the target are aligned. The hotspot phenomenon highlights the temperature differences between sunlit and shaded areas. However, due to the lack of adequate multi-angle observations and inaccurate portrayal or neglect of solar influence, the hotspot effect is often overlooked and has become a barrier for better inversion results at satellite scale. Therefore, hotspot effect needs to be better characterized, which here is achieved with a three-component model that distinguishes vegetation, sunlit and shaded soil temperature components and accounts for vegetation structure. Our work combines thermal infrared (TIR) observations from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the LEO (Low Earth Orbit) Sentinel-3, and two sensors onboard GEO (geostationary) satellites, i.e. the Advanced Himawari Imager (AHI) and Spinning Enhanced Visible and Infrared Imager (SEVIRI). Based on inversion with a Bayesian method and prior information associated with component temperature differences as constrained, the findings include: 1) Satellite observations throughout East Asia around noon indicate that for every 10 degrees change in angular distance from the sun, LST will on average vary by 0.6 K; 2) As a better constraint, the hotspot effect can benefit from multi-angle TIR observations to improve the retrieval of LST components, thereby reducing the root mean squared error (RMSE) from approximately 3.5 K, 5.8 K, and 4.1 K to 2.8 K, 3.5 K, and 3.1 K, at DM, EVO and KAL sites, respectively; 3) Based on a dataset simulated with a threedimensional radiative transfer model, a significant inversion error may result if the hotspot is ignored for an angular distance between the viewing and solar directions that is smaller than 30 degrees. Overall, considering the hotspot effect has the potential to reduce inversion noise and to separate the temperature difference between sunlit and shaded areas in a pixel, paving the way for producing stable temperature component products.

In the loess tableland, gully slope instability induces severe soil erosion and land degradation, yet the synergistic effects of dominant vegetation under varying restoration modes combined with dynamic rainfall regimes and topographic variations on gully slope stabilization mechanisms remain inadequately quantified. Therefore, the dominant vegetation species under natural (NR) and artificial restoration (AR) was chosen as the object. Through field sampling, root-soil complex mechanical experiments, and numerical simulations, the protection effect of dominant vegetation under different restoration modes combination with rainfall and topographic variations was investigated. The result revealed significant differences in basic soil physical properties, root morphological characteristics, root and root-soil complex mechanical properties among five dominant vegetated plots under the different restoration modes (P < 0.05). The soil properties in the Scop plot under AR were slightly better than those in the other plots. The roots in the Spp plot developed better under NR. The shear strength of Lespedeza bicolor Turcz. was the highest under NR. The tensile strength of Digitaria sanguinalis (L.) Scop. was greatest under AR. The tensile force and tensile strength of single roots exhibited a significant positive linear correlation and a significant negative exponential correlation, with root diameter, respectively (P < 0.01). For the unstable gully slopes (F-s < 1.0), maximum displacement occurred at the slope foot, where tensile shear failure dominated, while the interior experienced compressive yielding. The grey relational analysis identified rainfall intensity as the primary destabilizing factor, followed by dominant vegetation species, slope height, and slope gradient. Notably, when rainfall intensity reaches or exceeds 0.06 m/h, or when slope height exceeds 20 m combined with long-duration rainfall, the regulatory impacts of dominant vegetation under different restoration modes on the gully slope stability are substantially diminished and become negligible. This study provides a theoretical basis for gully slope protection and ecological environmental construction in loess tableland.

Early water stress detection is important for water use yield and sustainability. Traditional methods using the Internet of Things (IoT), such as soil moisture sensors, usually do not provide timely alerts, causing inefficient water use and, in some cases, crop damage. This research presents an innovative early water stress detection method in lettuce plants using Thermal Infrared (TIR) and RGB images in a controlled lab setting. The proposed method integrates advanced image processing techniques, including background elimination via Hue-Saturation- Value (HSV) thresholds, wavelet denoising for thermal image enhancement, RGB-TIR fusion using Principal Component Analysis (PCA), and Gaussian Mixture Model (GMM) clustering to segment stress regions. The leaves stressed areas annotated in the RGB image through yellow pseudo-coloring. This approach is predicated on the fact that when stomata close, transpiration decreases, which causes an increase in the temperature of the affected area. Experimental results reveal that this new approach can detect water stress up to 84 h earlier than conventional soil humidity sensors. Also, a comparative analysis was conducted where key components of the proposed hybrid framework were omitted. The results show inconsistent and inaccurate stress detection when excluding wavelet denoising and PCA fusion. A comparative analysis of image processing performed on a single- board computer (SBC) and through cloud computing over 5 G showed that SBC was 8.27% faster than cloud computing over a 5 G connection. The proposed method offers a more timely and accurate identification of water stress and promises significant benefits in improving crop yield and reducing water usage in indoor farming.