Snow avalanches pose a growing hazard in High Mountain Asia (HMA), yet their regional patterns are strongly governed by snow-climate regimes that are sensitive to both long-term warming and large-scale circulation variability. Using reanalysis-based meteorological and snow datasets (1980–2018), we develop a spatially continuous snow-climate zonation for HMA and classify the region into maritime (11.9%), transitional (4.1%), and continental (73.0%) snow-climate regimes. We detect a systematic shift toward warmer and wetter snow-climate characteristics, with the most pronounced changes along the southeastern HMA, where the transitional regime expands markedly. Variance decomposition further reveals non-stationary controls on zonation variability: temperature dominates temporal variability in the maritime and transitional regimes (explaining ~80% of their variance), whereas the continental regime is jointly regulated by temperature and snowfall, with a substantial contribution from the North Atlantic Oscillation (NAO) through its dynamical modulation of circulation and moisture transport. These findings provide a mechanistic, regime-aware framework for stratified avalanche-susceptibility modeling and for differentiated monitoring and risk management strategies across HMA under continued climate warming.

Highlights What are the main findings? What is the implication of the main finding?Highlights What are the main findings? What is the implication of the main finding?Abstract Volume quantification of proglacial lakes is a fundamental prerequisite for reliable hydrodynamic modeling and peak discharge estimation during glacial lake outburst floods (GLOFs). In this study, we integrated in situ bathymetric surveys of 10 proglacial lakes across the Himalaya and Nyainqentanglha ranges with a comprehensive regional dataset to derive optimized empirical models for lake volume and maximum depth. The predictive robustness of these models was rigorously validated using statistical error metrics and independent datasets. Comparative analysis with 14 established formulas demonstrates that our region-specific models yield superior performance in capturing local geomorphological characteristics. Leveraging these refined scaling relationships, we reconstructed the spatiotemporal volume changes in proglacial lakes across the study region from 1990 to 2020. Our analysis reveals significant lake expansion over the past three decades: lake volumes in the Western and Central Himalayas increased by 46.7% and 46.4%, respectively. Notably, the Eastern Himalayas exhibited a volume increase of 51.5%, while the Nyainqentanglha Mountains experienced a substantial expansion of approximately 92.9%. These findings provide critical parametric constraints for satellite-based hydrological monitoring and significantly enhance the reliability of GLOF hazard assessments in the Himalaya and Nyainqentanglha ranges.

The acceleration of climate change has led to an increase in the frequency of glacial lake outburst floods (GLOFs) in the Himalayas, resulting in significant devastation. InSAR data indicate that Qiangzongke Co, located in southwestern Tibet, China near the China-Nepal border, experienced considerable deformation of the adjacent terrain over recent decades, implying a potential landslide. Landslide entry into a glacial lake typically initiates surge waves that propagate forward, eroding the moraine dam and ultimately inducing a GLOF disaster. This study integrated multi-source data and hydrodynamic models to assess the potential GLOF process chain and downstream impact. The results indicate that the surface area of Qiangzongke Co has increased by 194

Ice-rich permafrost slopes on the Qinghai-Tibet Plateau (QTP) are becoming increasingly unstable under climatic warming. However, the instability mechanisms of permafrost slope associated with ground-ice melting remain insufficiently understood. This study integrates remote sensing, field investigations, and large-scale physical modeling to systematically elucidate the failure mechanisms of these slopes. A regional inventory of 1298 landslides shows a marked surge in slope instability in 2016, coinciding with record-high air temperatures. Field investigations further indicate that ice-rich layers are widely developed at the base of the active layer within failed slopes, exerting a critical control on slope stability. The physical experiment reproduced a three-stage retrogressive failure sequence-toe deformation, translational sliding, and headwall retreat-demonstrating that active-layer detachment (ALD) and retrogressive thaw slump (RTS) represent successive stages of a single interface-controlled failure process. The ice-rich layer acts both as a thermal buffer that delays heat penetration and as a hydrological barrier that promotes interfacial saturation and perched-water accumulation at the slope toe. Under thermal disturbance, phase change, meltwater redistribution, and interfacial weakening jointly initiate sliding and drive subsequent retrogressive collapse. Field evidence from ALD and RTS cases supports the experimental observations. The study therefore provides a multi-scale, process-based framework for understanding, monitoring, and mitigating thaw-driven slope instability in rapidly warming permafrost terrain.

The Tibetan Plateau, often referred to as the "Third Pole," exhibits heightened sensitivity and vulnerability to global climate change. It has been documented that the progressive retreat of high-altitude glaciers in this region, a phenomenon attributed to global warming, has led to the accumulation of extensive loose and unvegetated glacial till with buried ice (glacial till-ice composite). These unconsolidated deposits frequently serve as primary source materials for glacier-related hazards, including landslides and debris flows. Especially, the Parlung Tsangpo drainage basin in the southeastern portion of the Tibetan Plateau contains many glaciers with associated unconsolidated till. While significant efforts have been directed toward assessing the potential risks of glacier hazards in this area, the mechanical properties of glacial till-ice composite in response to climate warming remain poorly understood. To address this gap, a series of shear tests on glacial till-ice composite were conducted using a high-precision, temperature-controlled triaxial coupling test system, aiming to elucidate the shear deformation characteristics of glacial till-ice composite under varying temperatures and ice content levels. The findings reveal that the internal friction angle and cohesion of glacial till-ice composite undergo stage-wise changes with temperature, with the most pronounced reduction in strength observed within the -3 to -5 degrees C range. Furthermore, within this temperature interval, the cohesion of glacial till-ice composite demonstrates an exponential increase with rising ice content. In contrast to conventional frozen soils, glacial till-ice composites exhibit strength degradation over a narrower temperature range, characterized by accelerated strength attenuation and more significant strength loss during the deterioration process. To quantify these effects, Boltzmann and exponential attenuation functions were introduced to describe the influence of temperature and ice content on the shear strength of glacial till-ice composite. Based on the experimental results, a critical shear strength line for glacial till-ice composite was established as a function of temperature and ice content, and a strength degradation model incorporating these variables was developed. This model offers theoretical backing for disaster prevention and risk assessment of glacier debris flows.

The stability of basal-ice moraine slopes (BIMS) is increasingly threatened by climate warming, posing a significant geohazard in high-altitude regions. The degradation of buried basal ice, coupled with intense rainfall, induces complex thermo-hydro-mechanical (THM) interactions, yet the specific failure mechanisms remain insufficiently understood. This study provides direct experimental evidence using instrumented laboratory flume experiments to investigate the failure characteristics of BIMS under controlled thermal and rainfall conditions. By systematically analyzing volumetric water content, soil pressure, pore water pressure and deformation, we clarify the regulatory role of the ice-soil interface in slope instability. Results demonstrate this interface is the primary control, governing heat transfer and meltwater redistribution. We identify two distinct failure pathways: temperature-driven instability is progressive, evolving from initial slope-toe failure to large-scale misaligned sliding, where rising temperatures accelerate meltwater accumulation. In stark contrast, rainfall-induced instability is abrupt, characterized by rapid slope disintegration and surface flow-slips, with intense rainfall showing the potential to trigger debris flows. These findings elucidate the coupled THM failure mechanisms of BIMS, providing a robust scientific basis for process-based hazard assessment and risk mitigation in cold-region mountainous environments.

Understanding the dynamic interplay between floods and climate extremes in the Tibetan Plateau has long been constrained by scale fragmentation. Here, we elucidate the scale-dependent responses using historical observations and modelling. The average flood day and annual maximum daily discharge are governed by two complementary pathways: atmospheric input and catchment modulation. Spatially, flood drivers shift from cryosphere control in the west to monsoon control in the east. Crucially, low-order tributaries are dominated by the atmospheric source mechanism, responding instantaneously to high-intensity precipitation, while catchment modulators play a more important role in high-order mainstems. Furthermore, cross-watershed analysis further underscores that upstream temperature changes contribute 4.0% to downstream flood frequency and 6.4% to magnitude variability via hydrological connectivity. The scale-specific disparities, shaped by the synergistic effects of watershed hydrological processes, underlying surface heterogeneity, climate factor sensitivities, and climate-cryosphere interactions, establish a framework for alpine flood attribution and predictive models.

Climate change is a key driver of civilization evolution, with flooding, as a major natural hazard, exerting considerable influence on the development of settlements and human activities. Although previous studies have explored links between ancient cultural transformations and climate changes in the Chengdu Plain, the role of flooding in driving the migration of prehistoric settlements and their cultural centers remains unclear. To clarify the impact of flooding on the migration of prehistoric settlements in this region and to support the flood hypothesis for the collapse of Sanxingdui, we employ high-resolution topographic data and conduct hydrological simulations using the HEC-RAS model under three rainfall scenarios, combined with paleoclimate records, kernel density analysis and geomorphic flood index assessment. Our results indicate that during climatically unstable periods, relatively wet phases (4.2-3.95 ka BP and 3.15-2.90 ka BP) increased monsoon rainfall and resulted in frequent flooding. In response to flood hazards, prehistoric settlements adapted by migrating from low-elevation river floodplains to higher areas in the central region of the plain. For instance, peak of elevation kernel density increased from similar to 475 m to similar to 525 m between the early and late Baodun cultural phases, and from similar to 485 m to similar to 510 m during the transition from the Sanxingdui to the Shi'erqiao culture. After similar to 3.2 ka BP, the Sanxingdui site gradually declined and was eventually abandoned approximately 3.0 ka BP, with the cultural center shifting from a high flood-hazard area (mean flood depth 0.65 m) to the lower-hazard Jinsha site (mean flood depth 0.32 m). Concurrently, during the late Baodun and Shi'erqiao cultural periods, the shift in settlement layouts from concentrated, walled configurations to more dispersed patterns contributed to a certain enhancement of societal capacity to cope with flooding. This study provides new evidence supporting the flood hypothesis for the decline of Sanxingdui and demonstrates that flood hazard was a key driver of the migration of prehistoric settlements and cultural centers in the Chengdu Plain.

Abstract. Reliable avalanche forecasting is essential for protecting mountain communities and transportation corridors, but estimating avalanche occurrence from monitored meteorological and snowpack conditions remains difficult. Operational assessments often rely primarily on meteorological thresholds, although avalanche responses depend on both type-specific triggering process and the snowpack conditions. Using meteorological, pre-event snowpack, and avalanche observations collected during the 2024 and 2025 snow seasons, we analysed 37 recorded avalanche events, together with corresponding non-avalanche periods. Separate logistic-regression models were developed for dry- and wet-snow avalanches using the intensity and duration of the preceding snowfall or snowmelt process and background snow depth. Their performance was compared with otherwise identical models excluding snow depth. Under leave-one-out cross-validation, including background snow depth increased the area under the receiver operating characteristic curve from 0.72 to 0.83 for dry-snow avalanches and from 0.85 to 0.94 for wet-snow avalanches. For wet-snow avalanches, the true-positive rate increased from 0.79 to 0.92, while the false-positive rate decreased from 0.21 to 0.12. These results demonstrate that pre-event snowpack conditions provides predictive information beyond meteorological forcing alone and improves avalanche forecasting, particularly for wet-snow avalanches.

Debris flows pose significant threats to mountainous regions, necessitating accurate activity assessments for effective disaster mitigation and risk management. At a regional scale, debris flow studies have predominantly focused on susceptibility, without adequately addressing frequency and magnitude of these events. However, growing demands for hazard mitigation call for more detailed and comprehensive debris flow activity assessments. This study developed an integrated spatiotemporal debris flow activity assessment framework by combining spatial susceptibility modeling, temporal probability estimation, and potential event magnitude estimation. The assessment results for the Eastern Himalayan Syntaxis successfully identified historically active watersheds, including those impacted by catastrophic debris flows such as the 1953 Guxiang Glacier event. The study area was classified into five activity levels, with 37.8

Debris flows, consisting of mixtures of poorly sorted soil, rock and water, surge downstream along channelised paths, causing significant casualties and infrastructure damage. Mitigation typically involves installing barriers along potential flow paths to arrest the material. Laboratory flume experiments are established methods for investigating the physical mechanisms of debris flow mobility and flow-barrier interactions, although they often fail to capture the scale-dependent nature of debris flows reliably. To address this limitation, a new 190 m long, 6 m wide flume facility, the largest of its kind, has been constructed in Kunming, China. In this study, a test was conducted using a total volume of 180 m & sup3; of debris material to explore debris flow interactions with multiple flexible barriers, monitored by various sensors and instruments installed in the flume and the barriers. Results highlight the effectiveness of multiple flexible barriers in mitigating debris flows, showing progressive reduction of impact forces, landing distance and retention volume while moving downstream. Existing design criteria for estimating impact forces and barrier spacing in a multiple barrier system are validated. This research underscores the flume's capability to provide valuable, reproducible data, offering new insights into flow-barrier interactions, calibrating numerical models and contributing to the development of rational design guidelines.

The comprehensive pattern of the natural environment constitutes a complex system shaped by interactions among multiple natural elements, including geology, terrain, climate, hydrology, soil, and biodiversity. The regional structure that embodies this complexity is defined as the comprehensive natural terrestrial system. Consequently, this system provides an integrated perspective for understanding the overall characteristics of the natural environment and resources. Pakistan, with agriculture as its core economic sector, has a natural environment that is inherently linked to its topographic and climatic conditions. Its geographical environmental conditions are similar to those of China. Through analysis of Pakistan’s geological, geomorphological, climatological, hydrological, and vegetation conditions, we adopted the methodology of China’s comprehensive natural regionalization to establish a comprehensive natural terrestrial system scheme for Pakistan. Hierarchically, this scheme is divided into 3 major regions, 5 temperature zones, 8 humidity areas, and 23 natural regions. The scheme reveals the diversity of Pakistan’s natural environment and its three-dimensional geographical zonality characteristics. Furthermore, this study analyzes the ecological advantages, constraints, and resource development potential of each regional unit and proposes targeted strategies for ecological conservation and socioeconomic development. The scheme provides a scientific basis for the sustainable socioeconomic development of Pakistan.

Climate change intensifies global drought risk through altered precipitation, rising temperatures, and increased evaporative demand. Yet, drought assessments in Pakistan largely rely on single indices and overlook the combined effects of precipitation, temperature-driven moisture stress, and future socioeconomic exposure under SSP scenarios. To address these gaps, the present study provides a comprehensive assessment of future drought characteristics and their socioeconomic consequences in Pakistan, using high-resolution bias-corrected NEX-GDDP-CMIP6 models under four Shared Socioeconomic Pathways (i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Drought conditions and resultant socioeconomic exposure (population, gross domestic product (GDP), and cropland) were assessed utilizing the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) over a 12-mon accumulation interval for mid-century (2031–2060) and far-century (2071–2100). Results indicate strong spatial variability in projected drought patterns and socioeconomic exposure, with more pronounced changes under high-emission scenarios (SSP3-7.0 and SSP5-8.5) compared to low-emission pathways. The SPI projections indicate a moderate increase in long-duration precipitation-deficit droughts across northern Pakistan, with drought duration increasing by about 2–4 mon, whereas SPEI projections show substantially stronger warming-induced drought stress across the arid regions of southern Pakistan, with local drought duration increases exceeding 6 mon under high-emission scenarios. The central and northern Indus corridor experiences a notable rise in population and GDP exposure, with population exposure exceeding 10 million people-events and GDP exposure reaching approximately 50–100 billion USD-events by the late 21st century. Agricultural exposure increases across all scenarios, with a more pronounced expansion of hotspots in Punjab, Sindh, southern Khyber Pakhtunkhwa, and eastern Baluchistan under SSP3-7.0 and SSP5-8.5. Population exposure is primarily driven by demographic factors in the near term, while GDP exposure shows earlier and stronger sensitivity to climate-driven interactions. Overall, the findings indicate that Pakistan's drought risk is projected to increase markedly under future SSP scenarios, particularly affecting the agriculturally and economically vital Indus Basin. These results highlight the importance of integrating precipitation- and temperature-based drought metrics for robust risk assessment and emphasize the need for region-specific adaptation strategies focusing on water management, agricultural resilience, urban planning, and climate-responsive economic development.

Hydrological signatures (HS) have proven to be highly effective in calibrating physically-based hydrological models, enhancing their process consistency. However, their integration into parameter optimization for deep learning (DL)-based hydrological models has been limited. To address this gap, we propose a novel HS-informed framework that dynamically integrates HS into DL parameterization through a multi-task learning approach. This study evaluates the impact of HS integration on model performance using a large-scale, global hydrological data set. The HS-informed model achieved a significant performance improvement, with a median Nash-Sutcliffe Efficiency (NSE) of 0.739, compared to 0.666 for the baseline model across the test set. Notably, the most pronounced improvements in NSE were observed in hydrologically complex basins, including baseflow-dominated (+0.135), drought-prone (+0.148), and flood-prone basins (+0.159). Sensitivity analysis further revealed that the HS-informed model could leverage extended historical input data (over 120 days) to sustain robust performance (median NSE of 0.715) over a 30-day forecast period. Shapley Additive Explanations analysis highlighted two key mechanisms underlying these improvements: the enhanced recognition of long-term hydrological patterns through improved memory and a better representation of catchment heterogeneity by emphasizing non-climatic attributes. These findings demonstrate that integrating HS offers a superior approach to traditional point-error-based calibration in AI-driven hydrological modeling.

Rock mass weakness planes are mechanically unfavored discontinuities that control slope stability. However, their spatial pattern is poorly understood, limiting advances in regional landslide assessment. In this study, we propose a multi-explainable machine learning framework to predict the distribution of weakness planes and quantify their contribution to landslides. Focusing on southeastern Tibet, the study investigates 194 field outcrops of rock mass weakness planes, integrating geological section comparisons and driving factor analyses to reveal spatial heterogeneity and the evolvement of weakness plane. Rock mass weakness planes are denser in lithologically weak and structurally damaged rock masses, and less developed in gentler, sparsely faulted terrains. Comparative analysis of geological sections indicates that pre-existing discontinuities are essential preconditions for developing weakness planes, whereas precipitation acts as an activator. The controlling factors vary with lithology, indicating that material and structure govern how efficiently exogenic processes transform discontinuities into weakness planes. By considering weakness plane during susceptibility assessment, performances were improved (Recall increased by 4.6-6.8%, AUC increased by 3.1-5.6%). This work constructs the regional-scale model for continuous prediction of rock mass weakness planes. It links lithology-dependent formation mechanisms of rock mass weakness plane to slope instability, providing a process-based framework for interpreting landslide development and improving landslide susceptibility assessment.

Landslide susceptibility mapping (LSM) is crucial for disaster risk management and infrastructure planning, especially in mountainous regions prone to climatic variability. Traditional LSM methods often rely on a limited set of environmental parameters and use either statistical or machine learning (ML) models in isolation, overlooking the complex interactions between topography, climate, geology, and hydrology. Additionally, many studies neglect the importance of factor equilibrium and multicollinearity among these factors, limiting model reliability. This study proposes a comparative hybrid framework that evaluates both statistical and ML models to enhance the accuracy, robustness, and predictive power of LSM under varying climatic conditions. Using the N-15 Highway corridor in northern Pakistan, characterized by diverse topography, dynamic weather, and frequent landslides, we assess the influence of 21 conditioning factors related to terrain, climate, geology, and hydrology. Seven models were employed: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Decision Tree (DT), Artificial Neural Networks (ANN), Logistic Regression (LR), and Frequency Ratio (FR). Multicollinearity was tested using Pearson Correlation, Tolerance (TOL), and Variance Inflation Factor (VIF). Susceptibility maps were created for each model using the Landslide Susceptibility Index (LSI), and their performance was measured with metrics such as accuracy, precision, recall, F1-score, and area under the curve (AUC). Results indicate that ML models, particularly RF and XGBoost, outperformed traditional methods, with AUC values of 0.99 compared to 0.84 (FR)–0.95 (LR). High-risk zones identified by the models align with known landslide locations, highlighting the efficacy of ML models in LSM and their potential for disaster risk management.

To compare the utility of the frailty phenotype (FP), the FRAIL scale, and 5-item modified Frailty Index (mFI-5) in predicting postoperative adverse events after enhanced recovery after lumbar fusion surgery in older patients. This study prospectively included older patients (> 75 years) who underwent transforaminal lumbar interbody fusion from June 2019 to August 2021. Frailty status was evaluated using FP, the FRAIL scale, and mFI-5. The study investigated the associations between these three frailty tools and total adverse events, complications, and secondary outcomes. Multivariable logistic regression analysis was performed to identify predictors of total adverse events, complications, and secondary outcomes. Correlation analysis demonstrated that frailty assessed by the FP was significantly associated with an increased incidence of complications (55.7

Debris flows, which can be ferocious in mountainous regions, have been increasingly rampant in arid areas, threatening nearby residents and properties. Inadequate high-quality data, labor-intensive image interpretation, inefficient field surveys, and traditional singular-variable analyses underscore the necessity for an integrative and efficient approach to evaluate debris flow scales and activities. Thus, an integrative debris flow scale-activity assessment methodology is developed, composed of parameters commonly studied and those feasible for extraction from accessible sources, leveraging dynamic avulsion of fans with distinct morphometry and boulder visibility in images. Resultantly, channel number, fan size, maximum critical shear stress, and debris flow event frequency are acquired as potential index parameters via geomorphometric analysis and image segmentation. The composite index generation, established based on these parameters and principal component analysis, is highly effective and accurate, benefiting from program efficiency, interpretation-based error control, parameter validation, and data preservation. Comprehensively, three types of debris flow processes were identified in the study region, distinguished by water content and sediment composition. Parameter and index results reveal considerable spatial-temporal disparities, complex relationships, and clustering patterns (Moran's I = 0.08) across debris flow and aridity types owing to environmental variances. The index (degree) exhibits an increasing-decreasing transition from low-index hyper-concentrated flows to high-index debris floods and debris flows and declines to less impactful debris floods as aridity decreases due to different sediment and water discharge and source. Generally, high indices tend to concentrate in the southern and glacierized northern valleys, exhibiting medium impact ranges, high maximum critical shear stress, and dynamic channel activities regarding channel quantities and event frequencies. Hence, the results correlate with debris flow formation criteria and mechanisms, suggesting a reliable composite indicator for debris flow assessment and providing guidance for debris-flow risk mitigation.