Search Results (3,303)

While global climate change is fundamentally reshaping thermal regimes, capturing these shifts in topographically diverse regions remains a significant hurdle for standard gridded datasets. This study provides a comprehensive spatiotemporal analysis of 16 extreme temperature indices across Northern Carpathian from 1980 to 2024 using the E-OBS dataset. The QDM framework proved highly effective in neutralizing elevation-induced temperature biases, which reached up to 5.1 °C in raw E-OBS data. Beyond simple bias removal, the correction significantly improved the daily accuracy of the dataset, with RMSE values at high-altitude stations, such as the Chopok summit (1995 m), decreasing from 5.1 °C to 2.3 °C. Both Warm Days (TX90p) and Summer Days (SU) show near-perfect Field Coherence (C_f = 100% and 98%, respectively). A prominent feature of this temporal national average trend is its inherent asymmetry; the Annual Minimum (TNn) is climbing nearly twice as fast (+1.1 °C/decade) as the Annual Maximum (TXx) (+0.6 °C/decade), though the warming of these coldest nights is more localized (74.5% coherence). We also identified a clear signal of Elevation-Dependent Warming (EDW), with absolute maximums surging most aggressively in the Northern Carpathians at +1.6 °C/decade. Conversely, cold-tail indices like Ice Days are in a concurrent nationwide retreat (C_f = 97%), a shift that significantly reduces the physical window for winter tourism and alters the climatic envelope for fragile mountain ecosystems. Ultimately, these results position Slovakia as a high-sensitivity climate region where observed trends often outpace broader Central European averages, highlighting the urgent need for localized, nature-based adaptation strategies. Full article

Mountain regions are highly sensitive to climate warming, and the Himalayas are among the most vulnerable. Rising temperatures and changing hydroclimatic conditions are expected to increase forest fire risk, particularly in the Himalayan foothills and potentially at higher elevations. To investigate the meteorological conditions associated with forest fires in complex terrain, we analyzed 10 years (2011–2020) of km-scale (~3.3 km) ICON-CLM simulations together with MODIS/VIIRS fire observations, GFED5 fire emissions, and ERA5 reanalysis. Fire activity was examined across the Himalayan region (25–40° N, 70–115° E), with particular focus on elevation-dependent patterns. GFED5 indicates increasing black carbon and CO₂ emissions from elevations above 1 km, suggesting rising fire activity in Himalayan forests, while MODIS/VIIRS observations show that March–May is the peak fire season. Analysis of meteorological conditions during observed fire events shows that fires are associated with higher temperature, lower relative humidity, stronger winds, and little to no precipitation. A clear elevational shift was identified: compared with fires at 500–1000 m, fire events at 2500–3000 m occurred under relatively cooler and more humid conditions. Both ICON-CLM and ERA5 reproduce this pattern, but ICON-CLM generally represents fire event environments as warmer and drier than ERA5, highlighting the added value of km-scale regional climate modeling for understanding wildfire risk in the complex Himalayan terrain. Full article

Quantitative assessment of historical urban waterlogging on the Qinghai–Tibet Plateau (QTP) is severely hindered by the lack of early instrumental records. To bridge this data gap during the initial rapid urbanization period (1985–2003), this study proposes an integrated retrospective framework combining Large Language Models (LLMs)-based semantic mining, spatial reconstruction, and Extreme Gradient Boosting (XGBoost)- SHapley Additive exPlanations (SHAP) modeling under a Spatial Block Cross-Validation (SBCV) strategy. Historical disaster archives were transformed into spatially explicit training samples, enabling the reconstruction of a high-resolution urban waterlogging susceptibility atlas across the QTP. The results indicate that high-susceptibility areas are predominantly concentrated within urbanized river valleys and account for approximately 45% of the total urban built-up area across the QTP. The proposed framework achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.9793 under the SBCV strategy, indicating good spatial transferability within the study area. SHAP analysis revealed that geomorphic variables contributed more strongly than most climatic variables, highlighting the important role of a geomorphic confinement effect in shaping susceptibility patterns. Comparative analyses further suggest a spatial transition from basin-dominated accumulation patterns to increasingly valley-confined susceptibility distributions under stronger topographic constraints. In addition, surface albedo and land surface temperature were identified as influential predictors, likely reflecting integrated thermal-hydrological conditions associated with antecedent soil moisture and local urban thermal dynamics. This study establishes a historical risk baseline for the QTP and provides a reproducible and cost-effective framework for historical hazard assessment in other data-scarce mountainous and high-altitude regions. Full article

Accurate and spatially representative estimation of Global Horizontal Irradiance (GHI) is critical for solar energy planning in tropical regions characterized by strong atmospheric variability and complex topography. This study aims to evaluate the performance and robustness of four semi-empirical satellite-derived GHI models, Beyer, Perez, Hammer, and Rigollier, under heterogeneous tropical conditions in West Java, Indonesia. Hourly GHI data for 2022 were derived from GK2A satellite observations and validated against ground measurements from eight stations representing coastal, lowland, and mountainous areas. Model performance was assessed at annual and seasonal scales using relative Root Mean Square Error (rRMSE) and relative Mean Bias Error (rMBE). The results show significant variability in model performance across locations, with the average annual rRMSE computed per model and averaged over the eight stations being similar among models: 41.10% (Perez), 41.18% (Beyer), 42.44% (Hammer), and 42.49% (Rigollier). Perez showed the most consistent performance, with station-level rRMSE values ranging from 35.36% to 43.32% and rMBE ranging from −18.20% to 22.09%. Seasonal analysis indicates higher errors during the rainy season, 41.16% (Perez), 45.23% (Beyer), 42.74% (Hammer), and 46.34% (Rigollier), while lower errors were observed during the dry season, particularly for Beyer (36.16%) and Rigollier (36.29%). Spatial analysis indicates higher irradiance in coastal and lowland areas compared to mountainous regions. These findings emphasize the importance of climate- and topography-aware model selection for reliable solar resource assessment in tropical environments. Full article

Understanding how groundwater storage responds to extreme precipitation is essential for assessing aquifer resilience under climate variability. In this study, we developed a 1 km groundwater storage anomaly (GWSA) dataset for the Baiyangdian Watershed from 2002 to 2024 by downscaling GRACE observations with a Light Gradient Boosting Machine (LightGBM) model. The downscaled GWSA showed good consistency with independent hydrological datasets, including GLDAS and groundwater-level anomalies. Based on the downscaled product, we characterized long-term groundwater changes and quantified GWSA responses to extreme precipitation events (EPEs). Groundwater storage exhibited three distinct phases: rapid depletion before 2014 (−1.35 cm/yr), a slower decline during 2014–2019 (−1.04 cm/yr), and marked recovery after 2020 (+3.45 cm/yr). Spatially, GWSA generally increased from the southwest to the northeast of the watershed. Composite analysis of 11 EPEs revealed a delayed groundwater response, with the strongest signal occurring approximately two months after precipitation. Monthly effective precipitation was more closely associated with GWSA recovery than short-duration daily precipitation extremes, and the response was stronger in plains than in mountainous areas. These findings indicate that EPEs provide episodic recharge pulses, while sustained groundwater recovery depends on cumulative climatic inputs and human water-management influences. Full article

This paper examines the vulnerability and adaptive capacity of mountain communities in South and Central Asia, with specific reference to the Himalayas and the Central Asian highlands. Using a comparative framework, the study combines the Livelihood Vulnerability Index (LVI), LVI-IPCC, and the Livelihood Equity/Endowment Index (LEI) to measure multidimensional vulnerability. A mixed-methods approach combining household surveys and qualitative field evidence is used to analyze primary data from 600 households across four mountain regions: Leh (India), Sindhupalchok (Nepal), Batken (Kyrgyzstan), and Urgut (Uzbekistan). The results show that vulnerability is not explained only by climatic exposure; it is also associated with socio-economic conditions, institutional access, and livelihood assets. Leh and Sindhupalchok show higher vulnerability associated with water insecurity, food dependence, weak infrastructure, and climate variability, whereas Batken’s vulnerability is mainly linked to limited adaptive capacity. Urgut shows greater resilience associated with stronger adaptive capacity, despite persistent structural inequalities. The paper identifies financial access, social networks, and knowledge systems as important factors in strengthening resilience. It concludes that context-specific, inclusive, and asset-based policy interventions may help strengthen adaptive capacity and reduce vulnerability in fragile mountain ecosystems. Full article

Under global climate change, shifts in the suitable distribution of forest vegetation have become an important issue in ecology and biogeography, closely linked to forest biodiversity conservation and terrestrial ecosystem sustainable development. Birch forests are widely distributed across cold-temperate, temperate, and montane regions in China, but different birch forest types may vary in their environmental adaptations and spatial responses to climate change. In this study, three representative birch forest vegetation types in China, namely Betula utilis forest, Betula albosinensis forest, and Betula ermanii krummholz, were selected for comparative analysis. Based on vegetation distribution records and environmental variables, an optimized MaxEnt model was constructed using ENMeval to identify current suitable distribution patterns, key environmental drivers, and future habitat changes under climate change scenarios. The results showed that the three birch forest types differed markedly in current suitable distribution patterns. Betula utilis forest was mainly concentrated in the Qinling Mountains, Betula albosinensis forest showed a broader montane distribution pattern, and Betula ermanii krummholz was restricted to high-altitude or high-latitude cold habitats. Climatic factors were the dominant drivers of suitability, but the key environmental variables differed among the three vegetation types, indicating niche differentiation along temperature, precipitation, and elevation gradients. Under future climate scenarios, the suitable habitats of the three types showed type-specific changes in area, spatial stability, and centroid migration. Betula utilis forest and Betula albosinensis forest mainly exhibited regional spatial adjustment and partial expansion, whereas Betula ermanii krummholz showed stronger dependence on high-elevation cold habitats and more limited spatial adjustment capacity. These findings indicate that different birch forest vegetation types in China do not respond uniformly to climate change. The study provides a vegetation-type-specific basis for identifying stable suitable areas, potential expansion areas, and climate-sensitive habitats, and can support adaptive management and conservation planning for montane forest vegetation helping advance the implementation of Sustainable Development Goal 15 (SDG15) and long-term sustainability of mountain forest ecosystems under future climate change. Full article

Background: This study aims to delineate the present and projected suitable habitats for four dominant tick species in the Beijing–Tianjin–Hebei (BTH) region, providing a spatial basis for targeted tick-borne disease surveillance. Methods: We systematically reviewed the published literature and the Global Biodiversity Information Facility (GBIF) to compile tick occurrence records in the BTH region. A total of 167 geo-referenced occurrence records with verified coordinates were obtained for four dominant species: Haemaphysalis longicornis, Haemaphysalis concinna, Dermacentor silvarum, and Ixodes persulcatus. The MaxEnt model was applied with bioclimatic variables (WorldClim, 2.5 arc-min), elevation, slope, aspect, and NDVI. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) for within-species comparisons, complemented by the True Skill Statistic (TSS), Cohen’s kappa, and omission rate. Future projections (2021–2040, 2041–2060, 2061–2080, 2081–2100) were made under the SSP245 scenario using only climate variables, as the NDVI and topographic variables cannot be reliably forecast. Results: The four dominant tick species showed distinct distribution patterns: Hae. longicornis was widely distributed across the BTH region, whereas Hae. concinna, D. silvarum, and I. persulcatus were mainly found in the northern and northwestern mountainous areas. The primary environmental drivers were temperature, elevation, and the NDVI. MaxEnt models achieved good predictive performance (test AUC: 0.86–0.91; TSS: 0.72–0.88). Under future climate scenarios, suitable habitat centroids were projected to shift northwestward for Hae. longicornis (~57.6 km), D. silvarum (~71.1 km), and I. persulcatus (~50.0 km), and northeastward for Hae. concinna (~63.0 km) by 2081–2100. Conclusions: In this study, we identified current and future high-risk areas for four dominant tick species in the BTH region, providing a reproducible foundation for surveillance. Future projections should be interpreted with caution as they only account for climatic changes. Full article

Climate change and anthropogenic activities have substantially altered runoff generation and soil erosion processes. This study investigated the spatiotemporal patterns and influencing factors of the Potential Soil Erosion–Runoff Ratio (PERR), defined as the ratio of RUSLE-derived annual potential soil erosion amount to annual runoff volume, in the mountain–hill–plain transitional of Sanmenxia, China, from 1990 to 2015. Spatial statistical methods were integrated with comparative machine learning and SHAP-based interpretation. Among six candidate models, XGBoost achieved the best predictive performance, with R² values of 0.914 and 0.839 for the temporal and spatial holdout sets, respectively. PERR exhibited marked interannual fluctuations without a statistically significant monotonic trend, while the sequential Mann–Kendall test identified candidate temporal shifts around 1994–1995 and 2014. Spatially, persistent hot spots were concentrated in the southern mountainous and hilly regions, whereas persistent cold spots occurred mainly in the northern plains, revealing a clear geomorphic gradient. Slope, cropland cover, and elevation had the highest mean absolute SHAP values within the fitted model. A pronounced nonlinear transition in the modelled PERR response occurred near a slope of 17°, representing a model-derived and scale-dependent transition range rather than a universal physical threshold. These findings demonstrate the utility of explainable machine learning for identifying spatial heterogeneity and nonlinear controls in the runoff–potential erosion relationship and provide quantitative support for spatially targeted soil and water conservation. Full article

Vegetation net primary productivity (NPP) is a key indicator of terrestrial carbon sequestration and ecological restoration effectiveness. The karst mountainous region of Southwest China is characterized by fragmented terrain and high ecological vulnerability, making quantification of NPP dynamics and drivers essential for regional management. Using MOD17A3 NPP data (2000–2020), this study applied trend analysis, Hurst exponent analysis, partial correlation analysis, residual trend analysis, and Geodetector to investigate NPP spatiotemporal patterns and driving mechanisms in Guizhou Province. Results show a significant increasing trend in NPP (3.653 gC·m⁻²·a⁻¹, p < 0.01), with 78.61% of the area exhibiting growth and a spatial pattern of higher values in the south and lower values in the north. NPP shows persistence, indicating a continued increasing tendency. Along elevation gradients, NPP exhibits a unimodal pattern, peaking at 1000–1200 m, while growth rates increase with elevation and slope, with greater variability at higher altitudes. Temperature exerts a stronger and more extensive influence on NPP than precipitation, with significant correlations over 34.35% and 10.16% of the study area, respectively (p < 0.05). Residual trend analysis indicates that non-climatic factors accounted for a larger share of NPP variation (64.49%) than climatic factors (35.51%), with ecological restoration likely the leading non-climatic driver. Geomorphological type is the primary driver of spatial heterogeneity (q = 0.220), followed by precipitation, temperature, and land use, with interaction effects mainly showing nonlinear enhancement. These findings provide insights for ecological restoration and vegetation management in karst regions. Full article

Groundwater is an essential freshwater resource in the Western Himalayas, where increasing anthropogenic pressure and environmental variability are raising concerns regarding groundwater quality and water security. However, regionally integrated assessments of groundwater-quality variability across the Western Himalayan states remain limited. This study evaluates groundwater quality across Jammu and Kashmir, Himachal Pradesh, and Uttarakhand using groundwater-monitoring data obtained from the Central Ground Water Board (CGWB). A total of 338 observation wells monitored during 2019–2022 were analyzed using the weighted arithmetic Water Quality Index (WQI) based on Bureau of Indian Standards (BIS) and World Health Organization (WHO) drinking-water guidelines. Spatial and temporal variability were examined through hydrochemical, correlation, and geospatial analyses. The results reveal substantial regional and district-level variability in groundwater quality across the Western Himalayas. Groundwater in Himachal Pradesh and Uttarakhand is predominantly classified as excellent to good, whereas Jammu and Kashmir exhibit greater hydrochemical heterogeneity and localized groundwater deterioration. Elevated WQI values are concentrated within foothill and valley-transition districts, while high-altitude recharge zones generally maintain lower WQI values. Hydrochemical analyses indicate that groundwater-quality variability is primarily associated with mineralization processes, lithological controls, and localized anthropogenic influences. Temporal analysis further indicates moderate groundwater-quality improvement between 2019 and 2022, particularly in parts of Jammu and Kashmir. Overall, the findings demonstrate that groundwater systems across the Western Himalayas remain largely controlled by hydrogeological conditions but are increasingly modified by localized anthropogenic pressures. Strengthened groundwater monitoring, protection of recharge zones, and targeted management of vulnerable foothill and valley-transition environments will be essential for sustaining long-term water security in this climate-sensitive mountain region. Full article

Climate warming is accelerating permafrost degradation in alpine regions, promoting the development of thaw-related slope deformation through active-layer thickening, ground-ice thaw, and hydro-mechanical weakening. Permafrost degradation margins are particularly sensitive to climatic warming, where enhanced heat transfer and active-layer water migration can accelerate shallow slope instability; however, the underlying mechanisms require further investigation. This study investigates two representative freeze–thaw-related landslides in the western Qilian Mountains: an active-layer detachment developed in degraded discontinuous permafrost and a freeze–thaw-induced shallow creep landslide located near the lower limit of permafrost occurrence. UAV photogrammetry, electrical resistivity tomography, and SBAS InSAR were integrated to characterize geomorphic features, internal frozen ground conditions, and deformation patterns. The active-layer detachment shows strong subsurface heterogeneity, with residual high-resistivity frozen bodies separated by localized thawed zones. Its deformation is mainly concentrated in the upslope detachment zone and central depletion–transport zone, where meadow-mat cracking, turf stripping, and exposed mineral soil coincide with thawed corridors between discontinuous permafrost bodies. In contrast, the freeze–thaw-induced shallow creep landslide exhibits the largest deformation in the upper permafrost-margin sector, where weakly discontinuous permafrost persists, whereas deformation decreases downslope in the seasonally frozen ground sector. This study highlights the critical role of discontinuous permafrost, localized thawing, and active-layer water migration in promoting shallow slope deformation and suggests that permafrost degradation margins may become increasingly susceptible to freeze–thaw-induced landslide activity under continued climate warming. Full article

Wildfire regimes in mountain forest landscapes are shaped by complex interactions among topography, climate, vegetation, and human activity. However, predicting and interpreting fire occurrence in topographically heterogeneous regions remains challenging because fire–environment relationships vary strongly across elevation gradients and temporal scales. This study developed a hybrid machine-learning framework integrating an Information Value Model (IVM), Random Forest (RF), and Convolutional Neural Network (CNN) to decode elevation-mediated wildfire regimes in western Sichuan, China, a mountainous forest region characterized by strong vertical environmental gradients and high ecological conservation value. Multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) burned-area records, topographic variables, monthly meteorological data, vegetation indices, land-cover information, and human-accessibility proxies, were integrated at a 500 m spatial resolution. Environmentally comparable non-fire samples were generated from unburned vegetated pixels, and model training, RF-based feature selection, hyperparameter tuning using Particle Swarm Optimization (PSO), and performance evaluation were conducted within a nested spatial block cross-validation framework. The model produced continuous wildfire occurrence probabilities and showed strong discriminatory performance under the adopted validation protocol, with AUC values exceeding 0.95 across temporal datasets and low probability-error metrics. RF importance and correlation analyses identified mean temperature, elevation, and precipitation as the dominant predictors of wildfire probability. Spatial analyses revealed pronounced elevation-mediated differentiation in wildfire regimes: low-elevation valleys showed higher fire probability and stronger associations with human-accessibility proxies, whereas high-elevation plateau areas exhibited lower and more scattered fire patterns associated with climatic constraints. Seasonal and monthly analyses further showed that winter and spring fires dominated the regional fire regime, with risk intensifying during the pre-monsoon dry period. By combining probabilistic fire-risk mapping, spatial-context learning, and elevation-gradient interpretation, this study provides a transferable framework for understanding wildfire regimes in complex mountain forest landscapes. The findings support adaptive forest fire management, targeted monitoring, and risk zoning in mountainous regions where forest ecosystems, human activities, and conservation values intersect. Full article

Negative air ions (NAIs) are key ecological indicators of atmospheric cleanliness and forest ecosystem service functions, particularly in the context of forest wellness and ecotourism. However, long-term, high-frequency observations of NAIs across broad spatial scales remain scarce, limiting our understanding of its regional spatiotemporal dynamics and environmental controls. Here, we present a six-year (2018–2023) continuous, hourly monitoring dataset of NAI concentrations from 60 fixed forest sites across Zhejiang Province, a typical subtropical humid region in southeastern China. The provincial mean NAI concentration over the study period was 1672 ions·cm⁻³, with a pronounced “high around the periphery, low in the center” spatial pattern, with the mountainous southwestern areas consistently showing the highest concentrations and the central Jinqu Basin the lowest. On diurnal scales, NAIs exhibited a bimodal pattern with primary peaks at 7:00 and secondary peaks at 16:00, rather than a simple daytime–nighttime dichotomy. Seasonal dynamics showed significantly higher NAI in summer than in autumn and winter; however, the summer–winter difference was only ~25%, much smaller than the ratios reported for temperate regions. Interannually, NAI concentrations increased from 2018 to 2023 (average annual increase of 158 ions·cm⁻³), peaking during the 2020–2022 period, when anthropogenic emissions were substantially reduced. Using linear mixed-effects models, we identified relative humidity as the dominant positive driver of NAI variability, followed by wind speed as a negative modulator, and precipitation playing a minor role. These findings reveal the multi-scale spatiotemporal dynamics of NAIs in subtropical forests and underscore the overriding control of humidity over ion persistence. Our study provides a robust regional benchmark for background NAI levels in humid subtropical climates and offers direct scientific support for forest-based health resource planning and air quality assessment. Full article