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.

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.

Bedrock-soil layer slopes (BSLSs) are widely distributed in nature. The existence of the interface between bedrock and soil layer (IBSL) affects the failure modes of the BSLSs, and the seismic action makes the failure modes more complex. In order to accurately evaluate the safety and its corresponding main failure modes of BSLSs under seismic action, a system reliability method combined with the upper bound limit analysis method and Monte Carlo simulation (MCS) is proposed. Four types of failure modes and their corresponding factors of safety (Fs) were calculated by MATLAB program coding and validated with case in existing literature. The results show that overburden layer soil's strength, the IBSL's strength and geometric characteristic, and seismic action have significant effects on BSLSs' system reliability, failure modes and failure ranges. In addition, as the cohesion of the inclination angle of the IBSL and the horizontal seismic action increase, the failure range of the BSLS gradually approaches the IBSL, which means that the damage range becomes larger. However, with the increase of overburden layer soil's friction angle, IBSL's depth and strength, and vertical seismic actions, the failure range gradually approaches the surface of the BSLS, which means that the failure range becomes smaller.

The Tibetan Railway has introduced pressures on the fragile grassland ecosystems of the Tibetan Plateau. However, the impact of the railway on the carbon sequestration remains unclear, as existing studies primarily focus on in-situ vegetation observations. In this study, we extracted the start and end of the growing season (SOS, EOS) and maximum daily GPP (GPPmax) along the railway corridor from the satellite-derived Gross Primary Productivity (GPP) data, and quantified the extent and intensity of the railway's disturbance on these indicators. We further employed the Statistical Model of Integrated Phenology and Physiology (SMIPP) to translate these disturbances into annual cumulative GPP (GPPann). Results show that Tibetan Railway significantly influences grassland within 50-meters, causing earlier SOS (0.1086 d m-1), delayed EOS (0.0646 d m-1), and reduced GPPmax (0.0069 gC m-2 d-1 m-1) as the distance to the railway gets closer. The advanced SOS and delayed EOS contributed gains of 28.82 and 104.26 MgC y-1, but reduction in GPPmax accounted for a loss of 2952.79 MgC y-1. Railway-induced phenology-physiology trade-off causes GPPann loss of 2819.71 MgC y-1. This study reveals Tibetan Railway's impact on grassland carbon cycling, offering insights for grassland conservation and sustainable transportation infrastructure projects.

Earthquakes are common geological disasters, and slopes under seismic loading can trigger coseismic landslides, while also becoming unstable due to accumulated damage caused by the seismic activity. Reinforced soil slopes are widely used as seismic-resistant geotechnical systems. However, traditional geosynthetics cannot sense internal damage in reinforced soil systems, and existing in-situ distributed monitoring technologies are not suitable for seismic conditions, thus limiting accurate post-earthquake stability assessments of slopes. This study presents, for the first time, the use of a batch molding process to fabricate self-sensing piezoelectric geogrids (SPGG) for distributed monitoring of soil behavior under seismic conditions. The SPGG's reinforcement and damage sensing abilities were verified through model experiments. Results show that SPGG significantly enhances soil seismic resistance and can detect soil failure locations through voltage distortions. Additionally, the tensile deformation of the reinforcement material can be quantified with sub-centimeter precision by tracking impedance changes, enabling high-precision distributed monitoring of reinforced soil under seismic conditions. Notably, when integrated with wireless transmission technology, the SPGG-based monitoring system offers a promising solution for real-time monitoring and early warning in road infrastructure, where rapid detection and response to seismic hazards are critical for mitigating catastrophic outcomes.

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.

The development of thermokarst lakes on the Qinghai-Tibetan Plateau (QTP) serves as a prominent indicator of permafrost degradation driven by climate warming and increased humidity. However, quantitative observations of surface change and relationships between lakes and permafrost during thermokarst development remain inadequate. This study utilized long-term terrestrial laser scanning (TLS) to capture high-resolution data on the surface contour changes of the lake in the Beiluhe Basin over 3 years. Between June 2021 and September 2023, the area of BLH-B Lake increased by 19.23% to 6634 m2, with a maximum shoreline retreat distance of 14.37 m. Lake expansion exhibited pronounced seasonal characteristics, closely correlated with temperature and precipitation variations, with the most significant changes occurring during thawing periods. Notably, the lake expanded by up to 505 m2 during extreme rainfall events in the 2022 thawing period, accounting for 47.20% of the total expansion observed over 3 years. Integrated geophysical methods, including electrical resistivity tomography (ERT) and ground-penetrating radar (GPR), revealed substantial permafrost degradation, particularly along the northwestern and western shores, where talik formation occurred within 40 m of the lakeshore. Heat from groundwater flow within the active layer and direct solar radiation contributes to accelerated permafrost degradation around the lake. The integration of TLS with geophysical methods revealed both surface contour changes and subsurface permafrost conditions, providing a comprehensive view of the dynamics of thermokarst lakes. This integrated monitoring approach proves effective for studying thermokarst lake evolution, offering critical quantitative insights into permafrost degradation processes on the QTP and providing essential baselines for climate change impact assessment.

This study investigates the inter-annual variability of carbonaceous aerosols (CA) over Kolkata, a megacity in eastern India, using dual carbon isotopes (C-14 and C-13) alongside measurements of the optical properties of brown carbon (BrC). Sampling was conducted during the post-monsoon, winter, and spring seasons over two consecutive years (2020-21 and 2021-22). The analysis reveals that PM2.5 and CA concentrations were higher in 2020-21 (194 +/- 40 and 54 +/- 15 mu g m(-3), respectively) compared to 2021-22 (141 +/- 31 and 44 +/- 21 mu g m(-3)), likely due to higher precipitation in 2021-22. The contribution of biomass burning and biogenic sources to CA (f(bio_TC)) was slightly higher in 2020-21 (70 +/- 3 %) than in 2021-22 (68 +/- 3 %), with both years exhibiting a consistent decreasing trend from post-monsoon to spring. Observed lower values for oxidised CA proxies, such as the WSOC/OC ratio (0.41 +/- 0.08) and AMS-derived f(44) (0.13 +/- 0.02), throughout the study period suggest that surface CA over Kolkata primarily originates from local sources rather than long-range transport. The relative radiative forcing (RRF) also showed a clear reduction in the subsequent year; however, on average, the RRF of methanol-soluble BrC (16 +/- 6 %) was approximately three times higher than that of the water-soluble fraction (5.5 +/- 2.2 %), highlighting the substantial role of BrC in influencing regional radiative forcing. These findings underscore the substantial impact of local emissions over transported pollutants on Kolkata's ground-level air quality.

Suprapermafrost groundwater (SPG) plays a critical role in hydrological and ecological functioning of permafrost regions, yet its spatiotemporal dynamics and controlling mechanisms remain poorly understood on the Qinghai-Tibet Plateau (QTP). Here, we integrated in situ observations, geophysical surveys, and machine learning (ML) models (including XGBoost, LightGBM, and RandomForest) to investigate the seasonal variation, drivers, and projections of SPG dynamics in alpine meadow (AM) and alpine wet meadow (AWM) ecosystems. Results showed that SPG tables ranged from -1.1 to -0.1 m in AM and from -1.3 to -0.2 m in AWM during the warm season. SPG fluctuations were primarily driven by thaw depth (TD) and rainfall infiltration and exhibited similar seasonal patterns across both ecosystems. A greater TD was associated with a deeper SPG table, as deeper thawing expanded the unsaturated zone and enhanced vertical drainage, indicating an exponential relationship between TD and SPG table position, and a linear relationship with aquifer thickness. In contrast, rainfall infiltration increased shallow soil moisture and elevated SPG tables, with responses influenced by rainfall intensity, duration, and infiltration pathways. Spatial heterogeneity in SPG distribution was further shaped by vegetation structure and microtopographic variation. Furthermore, ML models projected that mean summer SPG table depths in the 2090s would increase by 0.06 m under SSP126 and 0.64 m under SSP585 in AWM ecosystems, and by 0.37 m under SSP126 and 0.87 m under SSP585 in AM ecosystems. These findings provide new insights into how climate warming affects hydrological processes in permafrost regions of the QTP.