Search Results (200)

Wildfire activity is intensifying under climate change, particularly in temperate East Asia where human-driven ignitions interact with extreme fire-weather conditions. This study examines wildfire risk during the March 2025 large wildfire event in Korea by applying explainable machine-learning models to assess ignition-prone environments and their spatial relationship with socio-environmental features relevant to exposure and management. CatBoost and LightGBM models were used to estimate wildfire susceptibility based on climatic, topographic, vegetation, and anthropogenic predictors, with SHAP analysis employed to interpret variable contributions. Both models showed strong predictive performance (CatBoost AUC = 0.910; LightGBM AUC = 0.907). Temperature, relative humidity, and wind speed emerged as the dominant climatic drivers, with ignition probability increasing under hot (>25 °C), dry (<25%), and windy (>6 m s⁻¹) conditions. Anthropogenic factors—including proximity to graves, mountain trails, forest roads, and contiguous coniferous stands (≥30 ha)—were consistently associated with elevated ignition likelihood, reflecting the role of human accessibility within pine-dominated landscapes. The socio-environmental overlay analysis further indicated that high-susceptibility zones were spatially aligned with arboreta, private commercial forests, and campsites, highlighting areas where ignition-prone environments coincide with active human use and forest management. These results suggest that wildfire risk in Korea is shaped by the spatial concurrence of climatic extremes, fuel continuity, and socio-environmental exposure. By situating explainable susceptibility modeling within an event-conditioned risk perspective, this study provides practical insights for identifying Wildfire Priority Management Areas (WPMAs) and supporting risk-informed prevention, preparedness, and spatial decision-making under ongoing climate change. Full article

The genus Satyrium (Orchidaceae) is a large, mostly sub-Saharan genus with a single species reported from Madagascar and Asia. Taxonomical complexity and high morphological diversity make the classification within the genus difficult to handle. In this study, we attempted to solve this problem using a comprehensive approach based on data from multiple sources. We combined morphological data from vegetative parts with data on flower structure using timescale phylogenetics conducted for both nuclear internal transcribed spacer (ITS) and plastid markers (matK, trnS-trnG, trnL, trnL-trnF). Phylogenetic studies confirmed most of the results of previous studies and led to the identification of six potential hybridization events within the genus. Morphological diversity often does not correspond to phylogenetic relationships within the genus, and many evolutionary lineages began to diverge only at the end of the early Miocene and in the late Miocene. The development of similar characteristics is the result of this diversification under the influence of similar environmental pressures. Reconstruction of the historical geographical range of Satyrium showed that the regions of South Africa and the mountainous areas of Eastern Africa played the most important role in the diversification of the genus. Full article

Symplocos is an ecologically important genus that plays vital roles in subtropical evergreen broad-leaved mountain forests, including contributing to nutrient cycling, providing shelter and habitats for various organisms, and supporting overall plant diversity across East and Southeast Asia. Many species exhibit high levels of endemism and sensitivity to environmental change. China, with its wide range of ecosystems and climatic zones, is home to 18 endemic Symplocos species. Studies revealed that global warming is driving shifts in species diversity, particularly in mountains. Our study explores the current and projected richness patterns of endemic Symplocos species in China under climate change scenarios, emphasizing the implications for conservation planning. We applied stacked species distribution models (SSDMs), using key bioclimatic and environmental variables to predict current and future habitat suitability for endemic Symplocos species, evaluated model performance through multiple accuracy metrics, and generated ensemble projections to assess richness patterns under climate change scenarios. To assess the spatial configuration and fragmentation patterns of the endemic species richness under current and future climate scenarios, landscape metrics were calculated based on classified richness maps. The produced models demonstrated high accuracy with AUC > 0.9 and TSS > 0.75, highlighting the critical role of bioclimatic variables, particularly precipitation and temperature, in shaping endemic Symplocos distribution. Our analysis identifies the current hotspots of Symplocos endemism along southeastern China, particularly in Zhejiang, Fujian, Jiangxi, Hunan, southern Anhui, and northern Guangdong and Guangxi. These areas are at high risk, with up to 35% of endemic Symplocos species richness predicted to be lost over the next 60 years due to climate change. The study predicts a high decrease in endemic Symplocos species richness, especially in South China (e.g., Fujian, Guangdong, Guizhou, Yunnan, southern Shaanxi), and mid-level decreases in East China (e.g., Heilongjiang, Jilin, eastern Inner Mongolia, Liaoning). Conversely, potential increases in endemic Symplocos species richness are projected in northern and western Xinjiang, western Tibet, and parts of eastern Sichuan, Guangxi, Hunan, Hebei, and Anhui, suggesting these regions may serve as future refugia for endemic Symplocos species. The analysis of the landscape structure and configuration revealed relatively minor but notable variations in the spatial structure of endemic Symplocos richness patterns under current and future climate scenarios. However, under the SSP585 scenario by 2080, the medium richness class showed a more pronounced decrease in aggregation index and increase in number of patches relative to other richness classes, suggesting that higher emissions may drive fragmentation of moderately rich areas, potentially isolating populations of Symplocos. These structural changes suggest a potential reduction in habitat quality and connectivity, posing significant risks to the persistence of endemic Symplocos populations, which underscores the urgent need for targeted smart-climate conservation strategies that prioritize both current hotspots and potential future refugia to enhance the resilience of endemic Symplocos forests and their ecosystems in the face of climate change. Full article

Salinity is a key abiotic stress limiting crop growth. Accurate quantification of carbon budgets and their environmental responses is critical for sustainable cotton production, yet regional-scale assessments remain scarce. To clarify the evolutionary patterns and driving mechanisms of soil organic carbon (SOC) in saline cotton fields of arid Central Asia, this study focused on Xinjiang and modified the RothC model by integrating salinity adjustment factors and vegetation carbon decomposition indices, simulating SOC dynamics (1980–2022) with multi-source data. Results showed the improved model achieved high accuracy in capturing SOC dynamics in salinized cotton fields. Spatially, SOC exhibited high levels south of the Tianshan Mountains and low levels in southwestern Xinjiang; temporally, it showed an overall fluctuating upward trend, though both high- and low-value zones displayed localized declines. Geodetector analysis revealed fertilizer application as the primary driver of SOC spatial variation, followed by straw return, precipitation, and temperature, with most factors showing synergistic enhancement effects. Human management (fertilization and straw return) is the core regulator of SOC, and its synergy with natural factors shapes SOC spatiotemporal patterns. The salinization-adapted RothC model provides a novel framework for arid cotton field SOC simulation, offering scientific support for carbon pool optimization and sustainable agriculture in arid regions. Full article

High Mountain Asia (25–40° N, 70–100° E) plays a critical role in sustaining water resources for nearly two billion people; however, the accurate estimation of precipitation remains challenging. Numerous gridded products have been developed, yet their performance across the region remains uncertain and is often analyzed only over small areas or short periods. This study provides a comprehensive evaluation of five major gridded precipitation datasets (ERA5, HARv2, GPCC, APHRODITE, and PERSIANN-CDR) over 1983–2007 throughout the entire domain through spatial intercomparison, validation against ground stations, and assessment against observed river discharge. Results show that reanalysis products (ERA5, HARv2) better capture spatial precipitation patterns, particularly along the Himalayas and Kunlun range, with HARv2 more accurately representing elevation-dependent gradients. Gauge-based (GPCC, APHRODITE) and satellite-derived (PERSIANN-CDR) datasets exhibit smoother fields and weaker orographic responses. In catchment-scale evaluations, reanalysis shows a superior performance, with ERA5 achieving the lowest bias, highest Kling–Gupta Efficiency, and best water-balance consistency. GPCC and PERSIANN-CDR underestimate discharge, and APHRODITE performs worst overall. No single dataset is optimal for all applications. Gauge-based datasets and PERSIANN-CDR are suitable for localized climatology in well-instrumented areas, while reanalysis products offer the best compromise between spatial realism and hydrological consistency for large-scale modelling in high-altitude regions where observations are limited. Full article

With global climate warming, reports of glacier lake outburst floods (GLOFs) have become increasingly frequent, highlighting the crucial need for robust GLOF sensitivity assessment methods for disaster prevention and mitigation. A reliable GLOF susceptibility assessment method was developed and applied in the Palong Zangbu River Basin in the Nagqu region of the Tibetan Plateau, integrating Digital Elevation Models (DEMs), glacier data, remote sensing imagery, and field survey data. The assessment evaluated the potential hazard levels of glacier lakes. Between 2000 and 2023, both the number and area of glacier lakes in the basin showed an increasing trend. Specifically, the number of glacier lakes larger than 0.08 km² increased by 32, with an area expansion of 14.17 km², corresponding to a growth rate of 43.95%. Based on the GLOF susceptibility assessment, 15 glacier lakes were identified as potentially hazardous in the study area, with the robustness of the method validated through ROC curve analysis. Therefore, it is recommended to regularly apply this method for GLOF susceptibility assessments in the Palong Zangbu River Basin, updating monitoring data and remote sensing imagery. This research provides valuable insights for GLOF susceptibility assessments in the High Mountain Asia (HMA) region. Full article

Glaciers in High-Mountain Asia, the so-called “Third Pole,” are critical water sources but remain poorly monitored due to rugged topography and limited accessibility. We present an integrated approach that combines remote sensing with ground-based observations to model ice melt of the Passu Glacier (Pakistan) from 5 August to 13 October 2023. Meteorological data from two automatic weather stations and ablation measurements from four stakes were used together with satellite-derived albedo (Landsat 8 OLI), surface temperature (Landsat 9 TIRS), and topography (ALOS AW3D30 DSM) to implement an enhanced T-index melt model accounting for net shortwave and longwave radiation. Model performance was evaluated against station and satellite data and ablation stake measurements using leave-one-out cross-validation. The estimated total ice melt volume was 16 million m³ w.e. during the monitoring period, with an average melt of 3.60 m w.e. The model reproduced observed stake ablation with an uncertainty of 0.48 m w.e. (9% of average measured melt). Elevation was identified as the dominant melt driver (β = −0.501, unique R² = 0.199), with aspect and slope exerting secondary influences through their effect on solar radiation and shading. Our findings demonstrate that combining minimal but strategically located field data with satellite products provides a physically consistent and scalable framework for glacier melt estimation in data-scarce regions of the Third Pole, with relevance for hydrological monitoring and climate adaptation. Full article

High-mountain regions such as the Ile-Alatau range of the Northern Tien Shan are highly sensitive to climatic fluctuations, where even minor variations in temperature and precipitation significantly influence glacier mass balance and hydrology. Despite this sensitivity, few long-term studies have examined the links between hydro-climatic trends, glacier retreat, and moraine lake development. This study investigates multi-decadal glacier and lake dynamics (1955–2024) in relation to observed climate variability, using an integrated hydro-climatic and remote-sensing approach. Temperature and precipitation records from four high-altitude meteorological stations were assessed using linear regression and the Mann–Kendall test, while glacier and lake extents were derived from aerial photographs and Landsat, Sentinel-2, and PlanetScope imagery across ten river basins. Results show statistically significant warming at all stations, with mean annual temperatures increasing by 0.14–0.28 °C per decade and summer temperatures by 0.15–0.30 °C, while precipitation remained stable or slightly decreased. Glacierized area decreased from approximately 269.6 km² in 1955 to 141.7 km² in 2021, representing a 47.4% reduction (≈−0.72% yr⁻¹) over six decades and underscoring the rapid regional cryospheric response to sustained climatic warming. Simultaneously, moraine-dammed lakes increased by 16–18% between 2017 and 2024. These trends highlight the dominant climatic control on glacier loss and lake evolution, emphasizing growing glacial lake outburst floods (GLOFs) and the need for adaptive water-resource management in Central Asia. Full article

Dynamical downscaling is an essential approach for bridging the gap between coarse-resolution global climate models and regional details required for climate impact assessment and sustainable development planning. Thailand, a climate-sensitive country in Southeast Asia, requires robust climate information to support its adaptation and resilience strategies. This study evaluated the Weather Research and Forecasting (WRF) model in dynamically downscaling selected Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations over Thailand during the baseline period of 1976–2005. A two-way nested WRF configuration was employed, with domains covering Southeast Asia (36 km) and Thailand (12 km) in the model. Model outputs were compared with gridded observations from the Climatic Research Unit (CRU TS), and spatial variations were analyzed across six administrative regions in Thailand. The WRF successfully reproduces broad climatological patterns, including the precipitation contrast between mountainous and lowland areas and the north–south gradient of temperature. Seasonal cycles of rainfall and temperature are generally well represented, although systematic biases remain, specifically the overestimation of orographic rainfall and a cold bias in high-elevation regions. The 12 km WRF simulations demonstrated improved special and temporal agreement with the CRU TS dataset, showing a national-scale wet bias (MBE = +17.14 mm/month), especially during the summer monsoon (+65.22 mm/month). Temperature simulations exhibited seasonal derivations, with a warm bias in the pre-monsoon season and a cold bias during the cool season, resulting in annual cold biases in both maximum (−1.25 C) and minimum (−0.80 C) temperatures. Despite systematic biases, WRF-CMIP5 downscaled framework provides enhanced regional climate information and valuable insights to support national-to-local climate change adaptation, resilience planning, and sustainable development strategies in Thailand and the broader Southeast Asian region. Full article

This study conducts a diagnostic analysis of an extremely heavy rainfall event and its causative factors that occurred in Libya, North Africa on 10 September 2023. The Weather Research and Forecasting (WRF) model was also employed to perform some sensitivity experiments for this heavy rainfall event and further reveal its causes. Results indicate that the primary synoptic system responsible for this extreme precipitation event was an extratropical cyclone (storm) named “Daniel”. During the formation and development of this cyclone, the circulation at the 500 hPa level from the eastern Atlantic to western Asia exhibited a stable “two troughs and one ridge” pattern, with a upper-level cold vortex over the eastern Atlantic, a high-pressure ridge over central Europe, and a cut-off low over western Asia, collectively facilitating the formation and development of this cyclone. As this cyclone moved southward, it absorbed substantial energy from the Mediterranean Sea; following landfall, the intrusion of weak cold air enabled the cyclone to continue intensifying. Meanwhile, the northwest low-level jet stream to the west of the extratropical cyclone moved alongside the cyclone to the coastal regions of northeastern Libya, where it converged with water vapor transport belts originating from the Ionian Sea, the Aegean Sea, and the coastal waters of northeastern Libya. This convergence provided abundant water vapor for the rainstorm event, and under the combined effects of convergence and orographic lifting on the windward slopes of the coastal mountains, extreme precipitation was generated. In addition, the atmosphere over the coastal regions of northeastern Libya exhibited strong stratification instability, which was conducive to the occurrence of extreme heavy precipitation. Although WRF successfully reproduced the precipitation process, the precipitation amount was underestimated. Sensitivity experiments revealed that both the topography in the precipitation area and the sea surface temperature (SST) of the Mediterranean Sea contributed to this extreme heavy precipitation event. Full article

The Puruogangri Ice Field (PIF) in the central Tibetan Plateau, known as the world’s Third Pole, is the largest modern ice field in the Tibetan Plateau and a crucial indicator of climate change. Although it was thought to be quiet, recent studies identified possible surging behaviors. But comprehensive velocity fields remain largely unknown. Here we present the first comprehensive and high spatiotemporal resolution 3D displacement field of the PIF from 2017 to 2024 using synthetic aperture radar (SAR) imaging geodesy. Using time-series InSAR and time-series pixel offset tracking and integrating ascending and descending Sentinel-1 SAR images, we invert the time-series 3D displacement over eight years. Our results reveal significant seasonal variations and three surging glaciers, with peak displacements exceeding 110 m in 12 days. Combined with ERA5 reanalysis and SAR backscatter coefficients analysis, we demonstrate that these surges are hydrologically controlled, likely initiated by damaged subglacial drainage systems. This study enhances our understanding of glacier dynamics in the central Tibetan Plateau and highlights the potential of using SAR imaging geodesy to monitor glacial hazards in High Mountain Asia. Full article

Heavy rainfall events in the southern Anhui region are the main meteorological disasters, often leading to floods and secondary disasters. This article explores the mechanisms supporting extreme precipitation by studying the spatiotemporal characteristics of heavy rainfall events during 2022–2024 and their related atmospheric circulation patterns. Using high-resolution precipitation data, ERA5 and GDAS reanalysis datasets, and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model analysis, the main sources and transport pathways of water that cause heavy rainfall in the region were determined. The results indicate that large-scale circulation systems, including the East Asian monsoon (EAM), the Western Pacific subtropical high (WPSH), the South Asian high (SAH), and the Tibetan Plateau monsoon (PM), play a decisive role in regulating water vapor flux and convergence in southern Anhui. Southeast Asia, the South China Sea, the western Pacific, and inland China are the main sources of water vapor, with multi-level and multi-channel transport. The uplift effect of mountainous terrain further enhances local precipitation. The Indian Ocean basin mode (IOBM) and zonal index are also closely related to the spatiotemporal changes in rainfall and disaster occurrence. The rainstorm disaster risk assessment based on principal component analysis, the information entropy weight method, and multiple regression shows that the power index model fitted by multiple linear regression is the best for the assessment of disaster-causing rainstorm events. The research results provide a scientific basis for enhancing early warning and disaster prevention capabilities in the context of climate change. Full article

As the Pamir Plateau is known as the “Water Tower of Central Asia”, accurate precipitation dataset is essential for the study of climate and hydrology in this region. Based on the monthly precipitation observations from 268 meteorological stations in the Eastern Pamir Plateau (EPP) during the April-to-September warm season of 2010–2024, this paper comprehensively evaluates the applicability of eight multi-source precipitation datasets in complex terrains by using statistical indicators, constructs a skill-weighted ensemble mean dataset (Skill-Ens), and analyzes the elevation-dependent characteristics of precipitation in the EPP. The research findings are as follows: (1) The warm-season precipitation in the EPP shows a significant elevation-dependent feature, with the maximum precipitation altitude (MPA) in the range of 2400–2800 m. Precipitation is reduced above this elevation range, but a second MPA may appear in the glacier area above 4000 m. (2) Among the studied eight datasets, the first-generation Chinese Global Land-surface Reanalysis (CRA40/Land) performs the best overall. A long-term (1979–2020) high-resolution (1/30°) precipitation dataset for the Third Pole region (TPHiPr) can most accurately capture the elevation-dependent characteristics of precipitation, while the satellite datasets are relatively poor in this respect. (3) The skill-weighted ensemble mean dataset (Skill-Ens) constructed in this study can significantly improve precipitation estimation (DISO = 0.35), especially in the MPA region, and can accurately depict the elevation-dependent characteristics of precipitation as well (CC = 0.92). In a word, this paper provides the applicable options for precipitation data in complex terrain areas. With the Skill-Ens, the limitation of the individual dataset has been compensated for, which is of significant application value in improving the accuracy of hydrological simulations in high-elevation mountainous areas. Full article

Extreme precipitation events, increasingly driven by climate change, are becoming more frequent and pose significant challenges to both the ecological environment and human society. Using the MSWEP data, this study constructed eight event-based extreme precipitation indicators so as to systematically analyze the spatiotemporal characteristics and dominant types of extreme precipitation across Central Asia and its three sub-regions from 1979 to 2023. The results revealed the following: (1) Extreme precipitation events exhibit a pronounced spatial preference for high-altitude areas, with the total number of events reaching up to 698 in these regions. (2) From 1979 to 1991, the frequency of extreme precipitation events has decreased in Central Asia (by 1.742 events per 13 years), while their duration has however increased (by 0.52 days per 13 years). The period from 1992 to 2009 experienced the most significant and widespread decline in the magnitude of extreme precipitation indicators. In contrast, from 2010 to 2023, all indicators—except for the event frequency (EF) and event intensity (EI)—have shown rising tendencies across the region. (3) Regarding the dominant event types, based on the proportion of extreme precipitation frequency across areas, the Southwestern Desert (SD) and northern Kazakhstan (NK) regions are characterized by a more prominent combination of rear-peak (TDP2) and front-peak (TDP1) events, whereas the southeastern mountains (SM) region is rather dominated by a combination of rear-peak (TDP2) and balanced-type (TDP3) events. (4) The EF and event duration (ED) are strongly associated with the Digital Elevation Model (DEM) and Aridity Index (AI). The spatial patterns of EF and ED are closely linked, with the sub-humid and mountainous regions demonstrating the highest frequency and longest duration of extreme precipitation events. Full article

Mountain glaciers are the most direct and sensitive indicators of climate change. In the context of global warming, monitoring changes in glacier elevation has become a crucial issue in modern cryosphere research. The Global Ecosystem Dynamics Investigation (GEDI) is a full-waveform laser altimeter with a multi-beam that provides unprecedented measurements of the Earth’s surface. Many studies have investigated its applications in assessing the vertical structure of various forests. However, few studies have assessed GEDI’s performance in detecting variations in glacier elevation in land ice in high-mountain Asia. To address this limitation, we selected the Southeastern Tibetan Plateau (SETP), one of the most sensitive areas to climate change, as a test area to assess the feasibility of using GEDI to monitor glacier elevation changes by comparing it with ICESat-2 ATL06 and the reference TanDEM-X DEM products. Moreover, this study further analyzes the influence of environmental factors (e.g., terrain slope and aspect, and altitude distribution) and glacier attributes (e.g., glacier area and debris cover) on changes in glacier elevation. The results show the following: (1) Compared to ICESat-2, in most cases, GEDI overestimated glacier thinning (i.e., elevation reduction) to some extent from 2019 to 2021, with an average overestimation value of about −0.29 m, while the annual average rate of elevation change was relatively close, at −0.70 ± 0.12 m/yr versus −0.62 ± 0.08 m/yr, respectively. (2) In terms of time, GEDI reflected glacier elevation changes at interannual and seasonal scales, and the trend of change was consistent with that found with ICESat-2. The results indicate that glacier accumulation mainly occurred in spring and winter, while the melting rate accelerated in summer and autumn. (3) GEDI effectively monitored and revealed the characteristics and patterns of glacier elevation changes with different terrain features, glacier area grades, etc.; however, as the slope increased, the accuracy of the reported changes in glacier elevation gradually decreased. Nonetheless, GEDI still provided reasonable estimates for changes in mountain glacier elevation. (4) The spatial distribution of GEDI footprints was uneven, directly affecting the accuracy of the monitoring results. Thus, to improve analyses of changes in glacier elevation, terrain factors should be comprehensively considered in further research. Overall, these promising results have the potential to be used as a basic dataset for further investigations of glacier mass and global climate change research. Full article