Search Results (272)

This study established a refined, distributed SWAT modeling framework that integrates elevation-band and snowmelt modules to reconstruct the alpine hydrological and sediment cycles of the Koshi River Basin (KRB) over the period 1990–2024, with climate scenarios constructed using the delta change approach. The KRB, a major transboundary watershed traversing China, Nepal, and India, was selected owing to its critical hydro-climatic role under the destabilizing “Asian Water Tower”; it generates substantial sediment yield, hosts the densest concentration of hydropower potential within the Ganges system, and spans an extreme vertical gradient from Mount Everest to the southern alluvial plains. Results reveal accelerated warming at a rate of 0.21 °C per decade and an overall warming–wetting trend, punctuated by an abrupt interdecadal shift around 2015. Precipitation dominated interannual streamflow variability, with enhanced rainfall triggering basin-wide sediment surges that overwhelmed the natural buffering capacity of the land surface. Conversely, rising temperatures intensified actual evapotranspiration, markedly depleting soil water and reducing total water yield and monsoon runoff, although sustained snow and glacier melt effectively elevated the dry-season low-flow baseline. The integrated climate forcing reshaped the disparity between hydrological extremes, imposing severe seasonal eco-hydrological stress that manifested as a pre-monsoon deficit in terrestrial green water and acute summer sediment outbursts for aquatic habitats. Furthermore, the flood regime exhibited an altered distribution, with mid-to-high frequency floods enhanced while low-frequency extreme flood peaks declined. The hydro-sedimentological regime consequently exhibits pronounced nonlinear responses to climate change, providing a critical, threshold-based scientific foundation for adaptive transboundary water resource management. Full article

The Gaizi River Basin, an alpine region in China crossed by the Karakoram Highway, is highly prone to glacier-related debris flows (GDF). Accurate debris flow susceptibility assessment in this high-altitude area remains challenging due to complex terrain, active tectonics, and dynamic glacial processes. This study develops a hybrid model integrating statistical methods and machine learning-based anomaly detection for debris flow susceptibility mapping. To address data noise, certainty factor (CF) distributions of debris flow predisposing factors (DFPFs) were derived via Locally Weighted Scatterplot Smoothing (LOWESS). The strength of the association between DFPFs and GDF susceptibility was evaluated using the mean residual between the raw and LOWESS-smoothed CF values. Multiple anomaly detection algorithms, including distance-based (L2 Norm), density-based (One-Class SVM), ensemble (Isolation Forest, RandNet), and GAN-based (WBiGAN-GP) methods, were tested on raw and CF-transformed data, using only the GDF inventory as the label. The CF-WBiGAN-GP model delivers the most balanced performance, excelling at identifying both high- and low-susceptibility zones. Results show that distance to stream, slope, and the topographic roughness and wetness indices are strongly associated with GDF susceptibility. Distance to glacier and precipitation appear less informative for direct susceptibility inference under our specific dataset and analytical setup. Full article

Niche and interspecific association are important components of community ecology and are of great significance for revealing the mechanisms of community assembly and its stability. In this study, the woody plant communities of Betula platyphylla Sukaczev forests in the Niyang River Basin of southeastern Qinghai–Tibet Plateau were taken as the research object. The niche, interspecific association, and community stability of dominant tree species in B. platyphylla forests were analyzed using the Levins index (B_L), Shannon index (B_S), Pianka index (O_ik), Schoener index (C_ik), variance ratio (VR), chi-square test, association coefficient (AC), Spearman rank correlation, and M. Godron stability methods. The results showed that a total of 71 woody plant species were recorded across 48 plots, mainly belonging to Rosaceae, Ericaceae, and Caprifoliaceae. B. platyphylla, Quercus aquifolioides Rehder & E. H. Wilson, Sorbus rehderiana Koehne, and Berberis gyalaica Ahrendt had relatively large niche breadths, indicating strong resource utilization ability and a wide range of spatial adaptation. They were the main constructive species and dominant species of B. platyphylla forest communities in this basin. The overall niche overlap of woody plant communities was relatively low, indicating relatively obvious differentiation in resource utilization among different species. Interspecific association analysis showed that the dominant species in the tree layer exhibited an overall significantly positive association, whereas those in the shrub layer exhibited an overall non-significantly positive association. The associations between species pairs were mainly non-significant, and the overall interspecific association was weak. Most species showed a relatively independent distribution pattern, reflecting weak interspecific competition within the community. Community stability analysis showed that the Euclidean distance between the tree layer and the theoretical stability point (20, 80) was 20.17, whereas that of the shrub layer was 27.98, indicating that the tree layer was more stable than the shrub layer. Overall, the community may not yet have reached a fully stable state. The results provide important references for biodiversity conservation, vegetation restoration, and sustainable forest management in alpine canyon ecosystems. Future studies should incorporate environmental factors such as soil properties and hydrothermal conditions to further reveal the ecological mechanisms driving community succession and stability. Full article

The accurate characterization of global precipitation regimes, encompassing not only the mean quantities but also the seasonal structure, concentration, and spatial heterogeneity, is essential for understanding the hydroclimatological dynamics and supporting climate-sensitive applications. This study presents a multi-scale precipitation climatology based on the IMERG Final Run V06B dataset (2001–2021) integrating satellite-derived monthly precipitation fields, unsupervised K-means clustering, Walsh–Lawler concentration metrics, and pixel-scale regime-dynamics indicators. The analysis identifies eight physically interpretable global precipitation regimes and six Italian sub-regional regimes characterized by distinct seasonal structures and precipitation persistence patterns. The resulting classifications exhibit a strong consistency with major atmospheric circulation domains, including monsoonal, mediterranean, continental, and equatorial precipitation regimes. A Hovmöller diagram highlights the seasonal northward migration of the Intertropical Convergence Zone (ITCZ) from approximately 5° S in January to 10° N in August. The K-means classification identifies eight physically interpretable global regimes, including a perhumid equatorial regime, a South-Asian monsoonal regime, a Southern-Hemisphere Mediterranean type, and a transitional autumn-peaked Mediterranean–Atlantic regime covering most of Italy and the broader Mediterranean basin. At the Italian scale, a dedicated K = 6 clustering reveals six distinct precipitation regimes, characterized by contrasting seasonal structures: the Alpine Convective regime, unique to the Alps and pre-Alpine foothills; the Po Valley Padano regime, the least seasonal regime in Italy; the Apennine Hybrid; the Tyrrhenian Mediterranean; the Adriatic Transition; and the Semi-arid Mediterranean regime, dominant across Sicily, Sardinia, and coastal southern Italy. The Walsh–Lawler Concentration Index increases markedly from north to south (~0.58), indicating a pronounced intensification of the temporal concentration of precipitation toward the Mediterranean climatic extreme. Overall, the study demonstrates the capability of high-resolution satellite climatologies to identify dynamically coherent precipitation-regime structures across multiple spatial scales and provides a quantitative baseline for future applications in hydrology, climate-risk assessment, and climate-change impact analysis. Full article

Revealing the spatial differentiation patterns of cultivated land multifunctionality contributes to the improvement of cultivated land protection policies. This study investigated the spatiotemporal differentiation characteristics and functional zoning of cultivated land multifunctionality in Alpine Valley Area from a topographic gradient perspective. An evaluation index system for cultivated land multifunctionality in Alpine Valley Area was constructed across four dimensions: production (PF), social (SF), ecological (EF), and landscape (LF) functions. Using Yulong County, Yunnan Province, as a case study, methods including kernel density analysis, standard deviation ellipse theory, topographic gradient analysis, and hierarchical clustering were employed to quantify the horizontal and topographic gradient characteristics of the multifunctionality of cultivated land from 2010 to 2020, thereby delineating functional zones. Results indicated: (1) Cultivated land multifunctionality shows clear topographically-dependent spatial differentiation: PF concentrates in central basins and northwest specialty agricultural zones, SF overlaps with production but with more dispersed high/low values, EF follows a “high in the center, low on the lateral areas” pattern, and LF remains relatively stable; (2) Significant hierarchical differences in cultivated land functions were observed along the elevation, slope, and terrain niche index (TNI) gradients. PF, EF, and LF generally decreased with increasing elevation, slope, and TNI, whereas the dominance of SF exhibited an inverted-V-shaped distribution along the gradient. (3) The study area was divided into five zones: Flat-Basin Agritourism Zone (FAZ), River-Valley Eco-Agriculture Zone (REZ), Sub-Alpine Specialty Agricultural Production Zone (SSAPZ), Sub-Alpine Steep Slope Integrated Management Zone (SSIMZ), and Mid-Mountain Steep Slope Ecological Conservation Zone (MSECZ), with differentiated strategies proposed for each. This study innovatively integrates a topographic gradient perspective, TNI, and hierarchical clustering to systematically evaluate the cultivated land multifunctionality in Alpine Valley Area, providing a new methodological framework for similar mountainous regions. Full article

The Xianglaqu River Basin, a major recharge area of the Xiagacuo endorheic lake basin on the Tibetan Plateau, provides an ideal setting for investigating hydrochemical evolution in alpine arid closed basins. In this study, 27 groundwater, spring-water, and surface-water samples collected from June to August 2023 were analyzed using correlation analysis, Piper diagrams, Gibbs diagrams, and ion-ratio methods. The results show that groundwater, spring water, and most surface water are predominantly of the HCO₃⁻–Ca·Mg type, indicating overall hydrochemical consistency across the basin. However, marked spatial differentiation occurs along the flow system: upstream waters are relatively simple and stable, whereas downstream and terminal surface waters show pronounced increases in Na⁺, Cl⁻, SO₄²⁻, and TDS, and some samples exhibit a tendency toward HCO₃⁻–Na facies. These patterns reflect progressive solute accumulation and terminal enrichment in the closed basin. Hydrochemical evolution is controlled mainly by water–rock interaction, with carbonate weathering as the dominant source of major ions, while silicate weathering, minor local saline-mineral dissolution, cation exchange, and evaporation concentration further influence water chemistry. Overall, the basin is characterized by local weathering release, along-path solute accumulation, and terminal evaporative enrichment. Full article

Under the influence of climate change and human activities, alpine grasslands in the Qinghai Lake basin have undergone a degradation trend over recent decades. In this context, investigating the distribution and stability of soil aggregates across varying degradation degrees of alpine grasslands, along with their driving factors, is critical for formulating sustainable management strategies to maintain grassland health and soil structural resilience in this ecologically sensitive region. In this study, plant and soil samples (0–20 cm) were collected at nine sites in the Qinghai Lake basin, each encompassing a non-degraded (ND), a lightly degraded (LD), and a heavily degraded (HD) grassland plot. The distribution and stability of mechanically stable aggregates and water-stable aggregates were evaluated using the dry-sieving and wet-sieving methods, respectively. The results showed that grassland degradation led to declines in plant above-ground and below-ground biomass, soil carbon, nitrogen, phosphorus, and microbial biomass carbon contents, and β-1,4-nacetylglucosaminidase activity, alongside an increase in soil pH. However, soil β-1,4-glucosidase and alkaline phosphatase activities exhibited no significant changes. The 2–0.25 mm fraction is the primary component of mechanically stable aggregates in alpine grasslands across three degradation levels. After degradation, neither the distribution nor the stability of mechanically stable aggregates exhibited significant changes. In terms of water-stable aggregates, the 2–0.25 mm fraction constituted the primary component in ND and LD, whereas the <0.053 mm fraction predominated in HD. Additionally, the mass proportions of the >2 mm and 2–0.25 mm size fractions were significantly lower in HD compared to ND, while the mass fraction of the <0.053 mm fraction was notably higher. The altered distribution of water-stable aggregates resulted in a significant decrease in mean weight diameter and a notable increase in the percentage of aggregate destruction, suggesting a reduced resistance of the soil to water erosion. Plant below-ground biomass, soil total organic carbon, and total nitrogen were identified as crucial factors modulating the dynamics of aggregate stability during grassland degradation. The findings of this study suggest that alpine grassland degradation in the Qinghai Lake basin reduces the water stability rather than the mechanical stability of soil aggregates. Full article

Groundwater is a critical lifeline for ecosystems and human settlements in arid and semi-arid regions, yet it is increasingly vulnerable to the dual pressures of extreme climatic conditions and intensifying anthropogenic activities. This study investigated 24 groundwater and 4 river water samples to discuss the hydrogeochemical evolution and water quality suitability in the Tianjun Basin, a typical high-altitude arid basin on the northeastern Tibetan Plateau. The results indicate that groundwater is mildly alkaline (pH: 7.65–8.35) and predominantly fresh (TDS: 233.77–1061.42 mg/L). Hydrochemical facies evolve from HCO₃-Ca type in upstream areas to Mixed HCO₃-Na·Ca and Cl-Na types. Hydrochemical analysis suggests that silicate weathering and carbonate dissolution are the dominant natural processes, while cation exchange further modifies the ionic composition. Notably, anthropogenic nitrogen (NO₃⁻ and NH₄⁺) contamination, primarily from domestic sewage in the Tianjun Basin, has significantly impacted groundwater quality. Health risk assessment shows that infants are the most vulnerable group, with 16.67% of samples posing a non-carcinogenic risk via the oral pathway. Regarding irrigation suitability, while sodium hazards are generally low, a significant salinity hazard is identified due to elevated electrical conductivity in the arid environment. This poses a substantial risk of secondary soil salinization, necessitating strict salt management strategies to preserve long-term land productivity. These findings provide critical insights for the sustainable management of fragile groundwater resources in extreme arid environments. Full article

We analyse pebble RFID tracing observations to investigate sediment transport dynamics in gravel-bed rivers using statistical modelling. This study examines a dataset of nearly 3500 tracer displacement measurements collected during 27 sediment-mobilizing events in a pre-Alpine reach in Italy. Our analysis follows three main steps, addressing tracer mobility patterns, event-scale transport dynamics, and reach-scale bedload inference. First, using Markov Chain analysis of state transitions on typical and high-magnitude transport events, we demonstrate that pebbles tend to maintain their mobility state between events, characterizing the between-event intermittency of bedload transport. A subsequent analysis of flow characteristics reveals that consecutive floods of similar magnitude exhibit increasing movement probability while maintaining similar virtual velocities. Finally, we train Gradient Boosting regression models to estimate distributions of pebble displacements and virtual velocities (defined, following common usage, as the ratio between the distance a tracer travels during a mobilising event and the duration of that event). Together with Monte Carlo propagation, these models are used to derive reach-scale volume estimates. The models identify flow rate and event duration as primary controls, while grain size has minimal influence within the sampled range of tracer dimensions. To strengthen our approach, we implement an extensive multi-stage validation process aimed at both single-tracer predictions and overall basin-scale movement estimates. The results indicate that high-magnitude transport events (12% of observations) contribute similar bedload volumes as typical events (88% of observations), highlighting the significant role of extreme events in total sediment transport. Model predictions yield bedload volume estimates that align well with independent measurements from a downstream sediment retention basin. Full article

Against the background of a warming and humidifying climate on the Qinghai–Tibet Plateau, increasing attention has been paid to the sustainability of water resources and ecosystems in the Qinghai Lake Basin. Investigating the characteristics of precipitation stable isotopes and moisture sources provides critical insights into the driving mechanisms of the regional hydrological cycle. In this study, precipitation samples collected at the Qinghai Lake Wetland Ecosystem National Observation and Research Station from June 2023 to October 2024 were analyzed for hydrogen (δ²H) and oxygen (δ¹⁸O) stable isotopes. The temporal variations of δ²H, δ¹⁸O, and deuterium excess (d-excess) were characterized, and their relationships with air temperature and precipitation amount were examined. In addition, a backward trajectory model was employed to identify the moisture sources of precipitation during the observation period. The results indicate that: (1) precipitation stable isotopes and d-excess exhibit pronounced seasonal variability, characterized by enrichment in summer and depletion in spring and autumn; (2) the Local Meteoric Water Line (LMWL) for the basin is defined as δ²H = 8.15δ¹⁸O + 38.71 (R² = 0.93), with both slope and intercept exceeding those of the Global Meteoric Water Line (GMWL); (3) precipitation isotopes show a discernible temperature effect but are jointly controlled by multiple moisture sources and meteorological factors; and (4) backward trajectory analysis combined with d-excess values reveals that precipitation moisture is primarily derived from westerly transport, while locally recycled moisture and continental air masses also exert significant influences. Overall, these findings reveal the multi-source driving mechanisms of the regional hydrological cycle and provide critical scientific support for understanding hydrological processes in alpine inland basins and their responses to future climate change, thereby contributing to the sustainable management of regional water resources. Full article

Livestock manure resources are abundant in the upper Yellow River basin on the eastern Tibetan Plateau, where rapeseed (Brassica napus L.) is grown under cold, short-season alpine conditions. To identify a suitable organic fertilizer substitution proportion, a two-year randomized complete block field experiment was conducted on Chestnut soil (Kastanozem) to compare mineral fertilization with 25%, 50%, 75%, and 100% replacement of mineral N by an organic fertilizer produced from composted cattle and sheep manure under equal total N, P, and K inputs. Grain yield was highest at 50% substitution, increasing by about 14% relative to mineral fertilization (p < 0.05), whereas 100% substitution slightly reduced yield. Increasing manure inputs enlarged soil organic carbon and total nutrient pools, but these increases were not accompanied by proportional increases in plant-available nutrients. Compared with mineral fertilization, 50% substitution increased available N, P, and K by about 18%, 34%, and 10%, respectively, and also increased the proportions of total N, P, and K present in available forms. Activities of the measured extracellular enzymes were generally 12–72% higher under 50% substitution than under mineral fertilization. A piecewise structural equation model indicated that yield improvement was associated mainly with greater nutrient uptake and recovery efficiency. Overall, moderate substitution best balanced nutrient accumulation, nutrient availability, efficiency, and productivity under the tested alpine conditions. Full article

Alpine wetlands in the Qinghai Lake Basin, located on the northeastern Qinghai–Tibetan Plateau, are ecologically important but highly vulnerable to climate change and anthropogenic disturbance. Traditional field-based surveys are labor-intensive and spatially constrained, underscoring the need for automated remote sensing approaches for large-scale wetland mapping. In this study, an object-based image analysis (OBIA) framework was developed by integrating Sentinel-2 optical imagery with Sentinel-1 synthetic aperture radar (SAR) data to classify two representative plateau wetland types: marsh meadows and inland tidal flats. Seven categories of features were evaluated, including spectral features, vegetation indices, water indices, red-edge features, topographic variables, radar backscatter, and geometric-textural metrics. The Separability and Thresholds (SEaTH) algorithm was employed for feature selection and optimization prior to classification using a Random Forest model. The results indicate that the incorporating geometric and textural features significantly improved classification performance, achieving an overall accuracy (OA) of 82.53% and a Kappa coefficient of 0.74. Moreover, the SEaTH-based feature optimization scheme yielded the best performance, with an OA of 86.24% and a Kappa coefficient of 0.79. Compared with the full feature set, this approach improved producer’s accuracy by 3.96–6.11% and increased overall accuracy by 1.48%. The proposed framework provides an effective and computationally efficient approach for mapping ecologically fragile alpine wetlands and offers valuable support for wetland conservation in the Qinghai Lake Basin. Full article

Soil moisture (SM) is a pivotal state variable of the terrestrial hydrosphere, modulating energy partitioning, agricultural productivity and extreme-event propagation. This study analyzes 43 years (1982–2024) of data to assess soil moisture (SM) dynamics in the Yellow River Basin (YRB). Results indicate a statistically significant basin-wide SM decline across weekly, monthly, and annual scales, with grid-scale slopes ranging from −2.26 × 10⁻⁴ to 8.32 × 10⁻⁵ m³ m⁻³ month⁻¹. Spatially, non-farm areas retain higher SM than cultivated lands, with a distinct upstream-to-downstream variability pattern. While alpine headwaters show moistening, pervasive drying characterizes mid- and lower-catchments. Critically, transitional landscapes are approaching tipping points, risking shifts into persistently wetter or drier stable states where minor perturbations could lock ecosystems into new conditions. This underscores the urgent need for targeted climate-adaptation interventions. Generalized additive modeling identifies surface net solar radiation, soil temperature, and vapor pressure deficit as dominant drivers across multiple temporal scales. Their respective contributions, averaged across the basin, accounted for 29.4%, 25.3%, and 23.0% of the explained variance. Additionally, actual evapotranspiration emerged as a significant driver on the weekly scale, particularly within the center of the basin. These findings enhance process-based understanding of SM variability and provide a scientific foundation for adaptive water-resource management in the YRB. Full article

The ongoing warming–wetting trend is profoundly reshaping water and land resources (WLR) in alpine regions, challenging their ecological functions. Focusing on the Yellow River source region above Huangheyan Station, we developed a synergistic WLR allocation framework explicitly oriented towards ecological function enhancement. We systematically assessed the spatiotemporal evolution of WLR and key ecological functions from 2000 to 2020, and projected future dynamics for 2030–2060 under four SSP scenarios. A multi-objective optimization model was established to minimize water shortage, maximize water conservation capacity (WCC), maximize vegetation water use efficiency (WUE), and minimize soil erosion amount (SEA), solved using the Non-dominated Sorting Genetic Algorithm II algorithm (NSGA-II). The results indicate significant ecological improvements over the past two decades (Net Primary Production (NPP) +14.3%, WCC +67.9%, SEA −34.1%). Critically, the optimized allocation schemes demonstrated substantial benefits across all future scenarios, enhancing WCC by 4.6–20.2%, improving WUE by 0.6–10.7%, and reducing SEA by 3.9–9.1%. This study offers a useful reference for coordinating ecological conservation and resource management in climate-sensitive and ecologically fragile alpine regions. Full article

Flood susceptibility mapping in high-altitude ungauged basins faces a structural dichotomy: physically based models often suffer from systematic biases due to uncertain satellite precipitation, whereas data-driven models are prone to overfitting and lack physical consistency in data-scarce regions. To resolve this, this study proposes a Physically constrained Particle Swarm Optimization–Random Forest (P-PDRF) framework, validated in the Lhasa River Basin. The core innovation lies in coupling a hydrological model with statistical learning by utilizing the maximum daily runoff depth as a “Relative Hydraulic Intensity Index.” This approach leverages the topological correctness of physical simulations to circumvent absolute forcing errors. Furthermore, a Physiographically Constrained Negative Sampling (PCNS) strategy and a PSO-optimized “Shallow Tree” configuration are introduced to enforce structural regularization against stochastic noise. Empirical results demonstrate that P-PDRF achieves superior generalization (AUC = 0.942), significantly outperforming standard Random Forest, Support Vector Machine, and Analytic Hierarchy Process models. Ablation studies confirm that the dynamic index outweighs the static Topographic Wetness Index in feature importance, effectively correcting topographic artifacts where static models misclassify arid depressions as high-risk zones. This study offers a scalable Physics-Informed Machine Learning solution for the global “Prediction in Ungauged Basins” initiative. Full article