Black carbon (BC) is a major short-lived climate pollutant (SLCP) with significant climate and environmentalhealth impacts. This review synthesizes critical advancements in the identification of emerging anthropogenic BC sources, updates to global warming potential (GWP) and global temperature potential (GTP) metrics, technical progress in characterization techniques, improvements in global-regional monitoring networks, emission inventory, and impact assessment methods. Notably, gas flaring, shipping, and urban waste burning have slowly emerged as dominant emission sources, especially in Asia, Eastern Europe, and Arctic regions. The updated GWP over 100 years for BC is estimated at 342 CO2-eq, compared to 658 CO2-eq in IPCC AR5. Recent CMIP6-based Earth System Models (ESMs) have improved attribution of BC's microphysics, identifying a 22 % increase in radiative forcing (RF) over hotspots like East Asia and Sub-Saharan Africa. Despite progress, challenges persist in monitoring network inter-comparability, emission inventory uncertainty, and underrepresentation of BC processes in ESMs. Future efforts could benefit from the integration of satellite data, artificial intelligence (AI)assisted methods, and harmonized protocols to improve BC assessment. Targeted mitigation strategies could avert up to four million premature deaths globally by 2030, albeit at a 17 % additional cost. These findings highlight BC's pivotal roles in near-term climate and sustainability policy.

Against the backdrop of global warming, the increasing spatiotemporal variability in precipitation patterns has intensified the frequency and risk of dry-wet abrupt alternation (DWAA) events in semi-arid regions. This study investigates the Hailar River Basin in northern China (1980-2019) and develops the Soil Moisture Concentration Index (SMCI) using daily soil moisture (SM) data simulated by the VIC hydrological model. A high-resolution temporal framework is introduced to detect DWAA events and evaluate the impact of precipitation pattern variations on dry-wet transitions in the basin. The results indicate: (1) Annual precipitation in the basin has significantly increased (0.47 mm y(-1) in the south, P < 0.05), while precipitation intensity follows a gradient pattern, increasing in the upstream (3.65 mm d1 y1) and decreasing in the downstream (-2.34 mm y(-1)). Additionally, the number of dry days and short-duration, high-intensity precipitation events has risen; (2) Soil moisture (SM) data simulated by the VIC model effectively capture DWAA events, showing significantly higher | SMCI| values downstream than upstream (P < 0.05) and indicating more intense dry-wet transitions in the downstream region. Furthermore, 78 % of the area exhibits an increasing trend in |SMCI|(1980-2019), with dry-to-wet transition events occurring more frequently than wet-to-dry events. For instance, in 2013, the maximum coverage area reached 48 % in a single day; (3) The random forest model highlights the spatial heterogeneity of DWAA driving factors: upstream water yield is the dominant factor, whereas downstream variations are closely associated with precipitation intensity (R-2 = 0.76) and the frequency of heavy rainfall days. Permafrost degradation and land use changes further heighten hydrological sensitivity in the downstream region. This study offers a transferable methodological framework for understanding extreme hydrological events and reveals that the driving mechanisms of DWAA are spatially heterogeneous, shifting from being dominated by terrestrial factors in the headwaters to meteorological factors downstream-a finding with significant implications for water resource management in other large, heterogeneous semi-arid basins.

The reasonable value of good gradation characteristic parameters is key in designing and optimising soil-rock mixed high fill embankment materials. Firstly, the DJSZ-150 dynamic-static large-scale triaxial testing instrument was used for triaxial compression shear tests on compacted skeleton structure soil-rock mixture standard specimens. The changes in strength and deformation indicators under different gradation parameters and confining pressure were analysed. Then, based on the Janbu empirical formula, relationships between parameters K, n, and (sigma 1-sigma 3)ult and the coefficient of uniformity Cu and coefficient of curvature Cc were explored. Empirical fitting formulas for Duncan-Chang model constants a and b were proposed, establishing an improved Duncan-Chang model for soil-rock mixtures considering gradation characteristics and stress states. Finally, based on significant differences in particle spatial distribution caused by gradation changes, three generalised models of matrix-block stone motion from different particle aggregation forms were proposed. Results indicate the standard specimen's strength and deformation indicators exhibit significant gradation effects and stress-state correlations. The improved Duncan-Chang model effectively simulates the stress-strain relationship curve under different gradations and confining pressure, with its characteristics explainable based on the matrix block stone motion generalised model.

Canopy reflectance (CR) models describe the transfer and interaction of radiation from the soil background to the canopy layer and play a vital role in the retrieval of biophysical variables. However, few efforts have focused on estimating soil background scattering operators, resulting in uncertainties in CR modelling, especially over sloping terrain. This study developed a canopy reflectance model for simulating CR over sloping terrain, which combines the general spectral vector (GSV) model, the PROSPECT model, and 4SAIL model coupled with topography (GSV-PROSAILT). The canopy reflectance simulated by GSV-PROSAILT was validated against two datasets: discrete anisotropic radiative transfer (DART) simulations and remote sensing observations. A comparison with DART simulations under various conditions revealed that the GSV-PROSAILT model captures terrain-induced CR distortion with high accuracy (red band: coefficient of determination $\lpar {\rm R 2} \rpar = 0.731$(R2)=0.731, root-mean-square error (RMSE) = 0.007; near infrared (NIR) band: $\rm R2 = 0.8319$R2=0.8319, RMSE = 0.0098). The results of remote sensing observation verification revealed that the GSV-PROSAILT model can be successfully used in CR modelling. These validations confirmed the performance of GSV-PROSAILT in soil and canopy reflectance modelling over sloping terrain, indicating that it can provide a potential tool for biophysical variable retrieval over mountainous areas.

Flash floods are often responsible for deaths and damage to infrastructure. The objective of this work is to create a data-driven model to understand how predisposing factors influence the spatial variation of the triggering factor (rainfall intensity) in the case of flash floods in the continental area of Portugal. Flash floods occurrences were extracted from the DISASTER database. We extracted the accumulated precipitation from the Copernicus database by considering two days of duration. The analysed predisposing factors for flooding were extracted considering the whole basin where each occurrence is located. These factors include the basin area, the predominant lithology, drainage density, and the mean or median values of elevation, slope, stream power index (SPI), topographic wetness index (TWI), roughness, and four soil properties. The Random Forest algorithm was used to build the models and obtained mean absolute percentage error (MAPE) around 19%, an acceptable value for the objectives of the work. The median of SPI, mean elevation and the area of the basin are the top three most relevant predisposing factors interpreted by the model for defining the rainfall input for flash flooding in mainland Portugal.

The root-knot nematode, Meloidogyne javanica, is one of the most damaging plant-parasitic nematodes, affecting chickpea and causing substantial yield losses worldwide. The damage potential and population dynamics of this nematode in chickpea in Ethiopia have yet to be investigated. In this study, six chickpea cultivars were tested using 12 ranges of initial population densities (Pi) of M. javanica second-stage juveniles (J2): 0, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64 and 128 J2 (g dry soil)-1 in a controlled glasshouse pot experiment. The Seinhorst yield loss and population dynamics models were fitted to describe population development and the effect on different measured growth variables. The tolerance limit (TTFW) for total fresh weight ranged from 0.05 to 1.22 J2 (g dry soil)-1, with corresponding yield losses ranging from 31 to 64%. The minimum yield for seed weight (mSW) ranged from 0.29 to 0.61, with estimated yield losses of 71 and 39%. The 'Haberu' and 'Geletu' cultivars were considered good hosts, with maximum population densities (M) of 16.27 and 5.64 J2 (g dry soil)-1 and maximum multiplication rate (a) values of 6.25 and 9.23, respectively. All other cultivars are moderate hosts for M. javanica; therefore, it is crucial to initiate chickpea-breeding strategies to manage the tropical root-knot nematode M. javanica in Ethiopia.

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.

Accurately reproducing the measured scattering matrix of black carbon (BC) through numerical simulations remains a challenge. Researchers have developed various morphological models of BC and computed their scattering matrices in attempts to replicate experimental measurements. However, prior simulation endeavors frequently encountered issues such as significant discrepancies with observational data, implausible particle shapes, or unsuitable computational parameters. In this study, we developed a fractal-based overlapping and necking model to represent the morphology of bare BC particles. We computed the scattering matrices for both individual particles and particle population using these models and compared the results with the previously reported measurements. Our findings revealed that the overlapping model reproduces the measured scattering matrix elements more accurately, whereas the necking model fails to achieve similar consistency. At a wavelength of 532 nm, the overlapping model yields F 22(pi)/F 11(pi) ranging from 0.80 to 0.99 for single particles and from 0.86 to 0.99 for particle population, both of which are much closer to the experimental observations than those of the necking model. In contrast, only a small subset of results from the necking model falls within the measured range. The overlapping model outperforms the necking model in reproducing the scattering matrix and should be preferred for representing bare BC particles. The established understanding provides useful guidance for retrieving microphysical parameters of BC from polarization features and for diminishing the uncertainties associated with its radiative forcing estimates.

Thawing-triggered slope failures and landslides are becoming an increasing concern in cold regions due to the ongoing climate change. Predicting and understanding the behaviour of frozen soils under these changing conditions is therefore critical and has led to a growing interest in the research community. To address this challenge, we present the first mesh-free smoothed particle hydrodynamics (SPH) computational framework designed to handle the multi-phase and multi-physic coupled thermo-hydro-mechanical (THM) process in frozen soils, namely the THM-SPH computational framework. The frozen soil is considered a tri-phase mixture (i.e., soil, water and ice), whose governing equations are then established based on u-p-T formulations. A critical-state elasto-plastic Clay and Sand Model for Frozen soils (CASM-F), formulated in terms of solid-phase stress, is then introduced to describe the transition response and large deformation behaviour of frozen soils due to thawing action for the first time. Several numerical verifications and demonstrations highlight the usefulness of this advanced THM-SPH computational framework in addressing challenging problems involving thawing-induced large deformation and failures of slopes. The results indicate that our proposed single-layer, fully coupled THM-SPH model can predict the entire failure process of thawing-induced landslides, from the initiation to post-failure responses, capturing the complex interaction among multiple coupled phases. This represents a significant advancement in the numerical modelling of frozen soils and their thawing-induced failure mechanisms in cold regions.

Thawing permafrost alters climate not only through carbon emissions but also via energy-water feedback and atmospheric teleconnections. This review focuses on the Tibetan Plateau, where strong freeze-thaw cycles, intense radiation, and complex snow-vegetation interactions constitute non-carbon climate responses. We synthesize recent evidence that links freeze-thaw cycles, ground heat flux dynamics, and soil moisture hysteresis to latent heat feedback, monsoon modulation, and planetary wave anomalies. Across these pathways, both observational and simulation studies reveal consistent signals of feedback amplification and nonlinear threshold behavior. However, most Earth system models underrepresent these processes due to simplifications in freezethaw processes, snow-soil-vegetation coupling, and cross-seasonal memory effects. We conclude by identifying priority processes to better simulate multi-scale cryosphere-climate feedback, especially under continued climate warming in high-altitude regions.