Search Results (183)

Under accelerating global warming, the northern slope of the Middle Kunlun Mountains in Xinjiang, China, has seen a marked rise in extreme rainfall, posing increasing challenges for flood risk management and water resources. To improve our predictive capabilities and deepen our understanding of the driving mechanisms, we combine the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) reanalysis, regional observations, and high-resolution Weather Research and Forecasting model (WRF) simulations to dissect the 14–17 June 2021, extreme rainfall event. A deep Siberia–Central Asia trough and nascent Central Asian vortex established a coupled upper- and low-level jet configuration that amplified large-scale ascent. Embedded shortwaves funnelled abundant moisture into the orographic basin, where strong low-level moisture convergence and vigorous warm-sector updrafts triggered and sustained deep convection. WRF reasonably replicated observed wind shear and radar echoes, revealing the descent of a mid-level jet into an ultra-low-level jet that provided a mesoscale engine for storm intensification. Momentum–budget diagnostics underscore the role of meridional momentum transport along sloping terrain in reinforcing low-level convergence and shear. Together, these synoptic-to-mesoscale interactions and moisture dynamics led to this landmark extreme-precipitation event. Full article

Abies nephrolepis and Picea jezoensis are native Pinaceae trees distributed in high mountainous regions of Northeast Asia (typically above ~1000 m a.s.l. on the Korean peninsula, northeastern China, Sakhalin, and the Russian Far East) and southern boreal forests, vulnerable to climate change and human disturbances, necessitating accurate habitat identification for effective conservation. While protected areas (PAs) are essential, merely expanding existing ones often fail to protect populations under human pressure and climate change. Using species distribution models with current and projected climate data, we mapped potential habitats across Northeast Asia. Spatial clustering analyses integrated with PA and land cover data helped identify optimal sites and priorities for new conservation areas. Ensemble species distribution models indicated extensive suitable habitats, especially in southern Sikhote-Alin, influenced by maritime-continental climates. Specific climate variables strongly affected habitat suitability for both species. The Kamchatka peninsula consistently emerged as an optimal habitat under future climate scenarios. Our study highlights essential environmental characteristics shaping the habitats of these species, reinforcing the importance of strategically enhancing existing PAs, and establishing new ones. These insights inform proactive conservation strategies for current and future challenges, by focusing on climate refugia and future habitat stability. Full article

The water regime in Pakistan’s northern region has experienced significant changes regarding hydrological extremes like floods because of climate change. Coupling hydrological models with remote sensing data can be valuable for flow simulation in data-scarce regions. This study focused on simulating the snow- and glacier-melt runoff using the snowmelt runoff model (SRM) in the Gilgit and Kachura River Basins of the upper Indus basin (UIB). The SRM was applied by coupling it with in situ and improved cloud-free MODIS snow and glacier composite satellite data (MOYDGL06) to simulate the flow under current and future climate scenarios. The SRM showed significant results: the Nash–Sutcliffe coefficient (NSE) for the calibration and validation period was between 0.93 and 0.97, and the difference in volume (between the simulated and observed flow) was in the range of −1.5 to 2.8% for both catchments. The flow tends to increase by 0.3–10.8% for both regions (with a higher increase in Gilgit) under mid- and late-21st-century climate scenarios. The Gilgit Basin’s higher hydrological sensitivity to climate change, compared to the Kachura Basin, stems from its lower mean elevation, seasonal snow dominance, and greater temperature-induced melt exposure. This study concludes that the simple temperature-based models, such as the SRM, coupled with improved satellite snow cover data, are reliable in simulating the current and future flows from the data-scarce mountainous catchments of Pakistan. The outcomes are valuable and can be used to anticipate and lessen any threat of flooding to the local community and the environment under the changing climate. This study may support flood assessment and mapping models in future flood risk reduction plans. Full article

The pilot study is devoted to the assessment of both the accumulation and spatial distribution of microplastics in the snow cover of the Zhetysu region. The height of snow cover in the study area varied from 4.0 to 80.5 cm, with a volume of melt water ranging from 1.5 to 143 L. The analysis of 53 snow samples taken at different altitudes (from 350 to 1500 m above sea level) showed the presence of microplastics in 92.6% of samples in concentrations from 1 to 12 particles per square meter. In total, 170 microplastic particles were identified. The main polymers identified by Raman spectroscopy were polyethylene (PE), polypropylene (PP), and polystyrene (PS). These are typical components of plastic waste. The spatial distribution of microplastics showed elevated concentrations near settlements and roads. Notable contaminations were also recorded in remote mountainous areas, confirming the significant role of long-range atmospheric transport. Particles smaller than 0.5 mm dominated, having high aerodynamic mobility and capable of long-range atmospheric transport. Quantitative and qualitative characteristics of microplastics in snow cover have been realized for the first time both in Kazakhstan and in the Central Asian region, which contributes to the formation of primary ideas and future approaches about microplastic pollution in continental inland regions. The obtained results demonstrate the importance of atmospheric transport in the distribution of microplastics. They indicate the need for further monitoring and microplastic pollution analyses in Central Asia, taking into account its detection even in hard-to-reach and remote areas. Full article

Reconstructing historical grazing intensity is essential for understanding long-term human–environment interactions in arid and semi-arid regions. However, historical documents often lack continuous, site-specific information on land use and grazing pressure. We present a high-resolution reconstruction of pastoral activity and hydrological evolution since 1560 AD using fungal spore assemblages from a 92 cm lacustrine-peat sequence from the Sichanghu (SCH) peatland on the northern slope of the Tianshan Mountains, Central Asia. Quantitative analysis of coprophilous fungal spores and principal component analysis (PCA) of spore influxes identify three distinct phases of pastoral intensity: gradual intensification from 1560 to 1730 AD, a sharp decline from 1730 to 1770 AD, and rapid intensification from 1770 AD to the present. These transitions are consistent with historical records of land use and human migration in Xinjiang. Additionally, fungal assemblages reveal a long-term drying trend at Sichanghu, broadly consistent with regional aridification in northwestern China. However, centennial-scale discrepancies in humidity between local and regional records—particularly during the late Little Ice Age—indicate that local hydrological responses were strongly influenced by anthropogenic disturbances. This study highlights the value of fungal spores, particularly influx-based interpretations, as robust indicators of both human activities and hydroclimatic variability. It also underscores the importance of integrating local and regional signals when reconstructing past environmental changes in sensitive dryland ecosystems. Full article

Ice thickness is a key parameter for glacier mass estimations and glacier dynamics simulations. Multiple physical models have been developed by glaciologists to estimate glacier ice thickness. However, obtaining internal and basal glacier parameters required by physical models is challenging, often leading to simplified models that struggle to capture the nonlinear characteristics of ice flow and resulting in significant uncertainties. To address this, this study proposes a convolutional neural network (CNN)-based deep learning model for glacier ice thickness estimation, named the Coordinate-Attentive Dense Glacier Ice Thickness Estimate Model (CADGITE). Based on in situ ice thickness measurements in the Swiss Alps, a CNN is designed to estimate glacier ice thickness by incorporating a new architecture that includes a Residual Coordinate Attention Block together with a Dense Connected Block, using the distance to glacier boundaries as a complement to inputs that include surface velocity, slope, and hypsometry. Taking ground-penetrating radar (GPR) measurements as a reference, the proposed model achieves a mean absolute deviation (MAD) of 24.28 m and a root mean square error (RMSE) of 37.95 m in Switzerland, outperforming mainstream physical models. When applied to 14 glaciers in High Mountain Asia, the model achieves an MAD of 20.91 m and an RMSE of 27.26 m compared to reference measurements, also exhibiting better performance than mainstream physical models. These comparisons demonstrate the good accuracy and cross-regional transferability of our approach, highlighting the potential of using deep learning-based methods for larger-scale glacier ice thickness estimation. Full article

This first inventory of the charophytes of the Caucasus region was compiled based on records from published references, online sources, a review of herbarium collections, and our own field collections. The documented Caucasian charophyte flora includes 27 species from six genera: 18 Chara species, 6 Nitella, 2 Tolypella, and 1 species each of Lamprothamnium, Nitellopsis, and Sphaerochara. Chara uzbekistanica, C. virgata, and C. contraria var. hispidula are newly recorded for the Caucasus. The high species richness of the genus Chara, the much less diverse genus Nitella, and a few species of Tolypella and Sphaerochara in the Caucasian charophyte flora are typical traits of Palearctic charophyte floras. In total, there are 10 species recorded in Armenia, 16 in Azerbaijan, 18 in Georgia, and 16 in the mountainous region of the North Caucasian Federal District of Russia. Most of the species have wide distributions; none are endemic to the Caucasus. One of the most commonly recorded species in the region, C. gymnophylla, is a usual feature of the Mediterranean and West Asia. The Caucasian charophyte flora can be described as unsurprising from a large-scale perspective, considering its species distribution ranges. However, the association of species makes the region specific at the scale of West Asia when comparing it to its large neighboring areas. Full article

East Asian Monosolenium tenerum is the only representative monotypic Monosoleniaceae and has been found for the first time in North Indochina in five provinces of Vietnam. All paleotropical localities, including those previously known in Indonesia, are situated not in the high mountains, as one might expect for the East Asian species occurring southward in East Asia, but in the lower altitude zones (i.e., in conditions physiognomically similar to where the species grows further north). A comparison of the bioclimatic parameters of locations where the species was found revealed clear similarities across all the Vietnamese localities of the species. The closest bioclimatic location to where the species has been found in Vietnam is in Nepal. The species reaches its greatest abundance in anthropogenically nitrified habitats, which may suggest that it is currently expanding to southern Indochina along the rural settlements in the catchments of the largest rivers and suggests that it may be discovered in Cambodia, Laos, and southern Vietnam in the near future. Full article

The diverse topography and mild monsoon climate in East Asia are considered to be important drivers for the long-term ecological success of the Tertiary relict ‘living fossil’ plants during the glacial/interglacial cycles. Here we investigated the phylogeographic pattern and demographic history of a hamamelidaceous Tertiary relict ‘living fossil’ tree (Parrotia subaequalis) endemic to the subtropical forests of eastern China, employing molecular data and ecological niche modeling. In the long evolutionary history, P. subaequalis has accumulated a high haplotype diversity. Weak gene flow by seeds, geographical isolation, and heterogeneous habitats have led to a relatively high level of genetic differentiation in this species. The divergence time of two cpDNA lineages of P. subaequalis was dated to the late Miocene of the Tertiary period, and the diversification of haplotypes occurred in the Quaternary period. Paleo-distribution modeling suggested that P. subaequalis followed the pattern of ‘glacial expansion-interglacial compression’. The Dabie Mountain and Yellow Mountain in Anhui Province and the Tianmu Mountain and Simin Mountain in Zhejiang Province were inferred to be multiple glacial refugia of P. subaequalis in East Asia and have been proposed to be protected as ‘Management Units’. Collectively, our study offers insights into the plant evolution and adaptation of P. subaequalis and other Tertiary relict ‘living fossil’ trees endemic to East Asia refugia. Full article

Against the background of global warming, forests across environmental gradients show distinct responses to climate change, necessitating research on tree growth patterns under specific conditions. Climate and competition are critical factors affecting tree growth, yet their combined effects across altitudinal gradients remain unclear, especially in arid regions such as Central Asia. This study investigated how climate and competition influence radial growth of Picea schrenkiana Fisch. et C.A.Mey. across altitudinal gradients (1500–2670 m) in the Middle Tianshan Mountains. Using dendroclimatology, competition indices, multivariate statistical analyses, and nonlinear models across 12 plots, we examined spatial variability in growth responses. Results revealed significant altitudinal differences in growth responses to climate and competition across altitudes. At low elevations, growth is primarily limited by water availability; drought indices and spring precipitation exert positive effects, while high temperatures inhibit growth. At mid-elevations, climate becomes the dominant driver, particularly spring temperature and precipitation playing key roles, while competition has no significant effect. At high elevations, temperature becomes the primary driver of growth; however, the overall sensitivity to climate is reduced compared to lower elevations. Multiple regression analyses confirm that water-related factors drive growth at lower and middle elevations, whereas temperature is the primary driver at higher elevations. Further model comparison indicates that while nonlinear models performed slightly better at mid-elevations, linear approaches similarly provided interpretable climate–growth relationships. This study demonstrates significant spatial variation in growth determinants, with water-driven controls dominating at lower elevations and competition effects ranging from significant to non-significant as altitude increases. Future warming may further intensify drought stress at lower elevations, and whether or not the weak positive responses currently observed at higher elevations will persist remains uncertain. These findings provide a scientific basis for sustainable management of arid mountain forests under climate change. Full article

Mountain forests in biodiversity hotspots show complex responses to climate and topographic gradients. However, the effect of synergistic controls of elevation and climate on Net Primary Productivity (NPP) dynamics remain insufficiently quantified in complex mountains. Southwest China’s mountains are Asia’s most biodiverse temperate region with pronounced vertical ecosystem stratification, representing a critical continental carbon sink. This study investigated the spatiotemporal dynamics and driving mechanisms of NPP in Southwest China’s typical mountain ecosystems over the past three decades using a high-resolution modeling framework integrated with relative importance analysis, a Geodetector, and an elevation-dependent model. The results showed that (1) NPP revealed a significant increasing trend, rising from 634 ± 325 to 748 ± 348 g C m⁻² yr⁻¹ (mean rate 4 g C m⁻² yr⁻¹) from 1990 to 2018. Spatially, the most rapid increases occurred in eastern regions. (2) Rising CO₂ and climate warming (dominate 17% regions) drove interannual NPP growth, with elevation thresholds dictating driver dominance. The CO₂ governed low elevation, while temperature controlled higher elevation (>4800 m). (3) The elevation-dependent model revealed a more complex and nonlinear relationship between NPP and elevation, identifying three distinct phases: the saturation phase (<500 m) with negligible decay of NPP; the transition phase (500–3500 m) with linear decline (NPP loss of 29 g C m⁻² yr⁻¹ per 100 m); and the collapse phase (>3500 m) with continuously attenuated NPP losses (NPP average loss of 10.5 g C m⁻² yr⁻¹ per 100 m) reflecting high-elevation vegetation adaptation to extreme conditions. (4) Land cover dominated NPP spatial heterogeneity and was amplified by interactions with elevation and temperature, highlighting a vegetation–climate–topography coupling mechanism that critically shapes productivity patterns. Biodiversity-rich widespread mixed forests underpinned the region’s high productivity. Mountain protection should focus on protecting existing evergreen forests from fragmentation, while forestation should prioritize the establishment of biodiversity-rich mixed forest. These findings established a comprehensive framework for spatiotemporal analysis of driving mechanisms and enhanced the understanding of NPP dynamics in complex mountain ecosystems, informing sustainable management priorities in mountain regions. Full article

The trematodes Fasciola hepatica and F. gigantica are transmitted by lymnaeid snails and cause fascioliasis in livestock and humans. Human infection is emerging in southern and southeastern Asia. In India, the number of case reports has increased since 1993. This multidisciplinary study analyzes the epidemiological scenario of human infection. The study reviews the total of 55 fascioliasis patients, their characteristics, and geographical distribution. Causes underlying this emergence are assessed by analyzing (i) the climate change suffered by India based on 40-year-data from meteorological stations, and (ii) the geographical fascioliasis hotspots according to archeological–historical records about thousands of years of pack animal movements. The review suggests frequent misdiagnosis of the wide lowland-distributed F. gigantica with F. hepatica and emphasizes the need to obtain anamnesic information about the locality of residence and the infection source. Prevalence appears to be higher in females and in the 30–40-year age group. The time elapsed between symptom onset and diagnosis varied from 10 days to 5 years (mean 9.2 months). Infection was diagnosed by egg finding (in 12 cases), adult finding (28), serology (3), and clinics and image techniques (12). Climate diagrams and the Wb-bs forecast index show higher temperatures favoring the warm condition-preferring main snail vector Radix luteola and a precipitation increase due to fewer rainy days but more days of extreme rainfall, leading to increasing surface water availability and favoring fascioliasis transmission. Climate trends indicate a risk of future increasing fascioliasis emergence, including a seasonal infection risk from June–July to October–November. Geographical zones of high human infection risk defined by archeological–historical analyses concern: (i) the Indo-Gangetic Plains and corridors used by the old Grand Trunk Road and Daksinapatha Road, (ii) northern mountainous areas by connections with the Silk Road and Tea-Horse Road, and (iii) the hinterlands of western and eastern seaport cities involved in the past Maritime Silk Road. Routes and nodes are illustrated, all transhumant–nomadic–pastoralist groups are detailed, and livestock prevalences per state are given. A baseline defining areas and seasons of high infection risk is established for the first time in India. This is henceforth expected to be helpful for physicians, prevention measures, control initiatives, and recommendations for health administration officers. Full article

The southern slope of the Tianshan Mountains features complex terrain and an arid climate, yet paradoxically experiences frequent extreme precipitation events (EPEs), which pose significant challenges for weather forecasting. This study investigates an EPE that occurred from 20 to 21 August 2019 using multi-source data to examine circulation patterns, mesoscale characteristics, moisture dynamics, and energy-instability mechanisms. The results reveal distinct spatiotemporal variability in precipitation, prompting a two-stage analytical framework: stage 1 (western plains), dominated by localized convective cells, and stage 2 (northeastern mountains), characterized by orographically enhanced precipitation clusters. The event was associated with a “two ridges and one trough” circulation pattern at 500 hPa and a dual-core structure of the South Asian high at 200 hPa. Dynamic forcing stemmed from cyclonic convergence, vertical wind shear, low-level convergence lines, water vapor (WV) transport, and jet-induced upper-level divergence. A stronger vorticity, divergence, and vertical velocity in stage 1 resulted in more intense precipitation. The thermodynamic analysis showed enhanced low-level cold advection in the plains before the event. Sounding data revealed increases in precipitable water and convective available potential energy (CAPE) in both stages. WV tracing showed vertical differences in moisture sources: at 3000 m, ~70% originated from Central Asia via the Caspian and Black Seas; at 5000 m, source and path differences emerged between stages. In stage 1, specific humidity along each vapor track was higher than in stage 2 during the EPE, with a 12 h pre-event enhancement. Both stages featured rapid convective cloud growth, with decreases in total black body temperature (TBB) associated with precipitation intensification. During stage 1, the EPE center aligned with a large TBB gradient at the edge of a cold cloud zone, where vigorous convection occurred. In contrast to typical northern events, which are linked to colder cloud tops and vigorous convection, the afternoon EPE in stage 2 formed near cloud edges with lesser negative TBB values. These findings advance the understanding of multi-scale extreme precipitation mechanisms in arid mountains, aiding improved forecasting in complex terrains. Full article

The Qinghai–Tibet Plateau (QTP), a critical hydrological regulator for Asia through its extensive glacier systems, high-altitude lakes, and intricate network of rivers, exhibits amplified sensitivity to climate-driven alterations in precipitation regimes and ice mass balance. While the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have revolutionized monitoring of terrestrial water storage anomalies (TWSAs) across this hydrologically sensitive region, spatial resolution limitations (3°, equivalent to ~300 km) constrain process-scale analysis, compounded by mission temporal discontinuity (data gaps). In this study, we present a novel downscaling framework integrating temporal gap compensation and spatial refinement to a 0.25° resolution through Gated Recurrent Unit (GRU) neural networks, an architecture optimized for univariate time series modeling. Through the assimilation of multi-source hydrological parameters (glacier mass flux, cryosphere–precipitation interactions, and land surface processes), the GRU-based result resolves nonlinear storage dynamics while bridging inter-mission observational gaps. Grid-level implementation preserves mass conservation principles across heterogeneous topographies, successfully reconstructing seasonal-to-interannual TWSA variability and also its long-term trends. Comparative validation against GRACE mascon solutions and process-based hydrological models demonstrates enhanced capacity in resolving sub-basin heterogeneity. This GRU-derived high-resolution TWSA is especially valuable for dissecting local variability in areas such as the Brahmaputra Basin, where complex water cycling can affect downstream water security. Our study provides transferable methodologies for mountainous hydrogeodesy analysis under evolving climate regimes. Future enhancements through physics-informed deep learning and next-generation climatology–hydrology–gravimetry synergy (e.g., observations and models) could further constrain uncertainties in extreme elevation zones, advancing the predictive understanding of Asia’s water tower sustainability. Full article

Central Asia, as a chronically water-stressed region marked by extreme aridity, faces significant environmental challenges from intensifying desertification and deteriorating ecological stability. The region’s vulnerability to shifting precipitation regimes and extreme hydrometeorological events has been magnified under anthropogenic climate forcing. Although global climate models (GCMs) remain essential tools for climate projections, their utility in Central Asia’s complex terrain is constrained by inherent limitations: coarse spatial resolution (~100–250 km) and imperfect parameterization of orographic precipitation mechanisms. This investigation advances precipitation modeling through deep learning-enhanced statistical downscaling, employing convolutional neural networks (CNNs) to generate high-resolution precipitation data at approximately 10 km resolution. Our results show that the deep learning models successfully simulate the high center of precipitation and extreme precipitation near the Tianshan Mountains, exhibiting high spatial applicability. Under intermediate (SSP-245) and high-emission (SSP-585) future scenarios, the increase in extreme precipitation over the next century is significantly more pronounced compared to mean precipitation. By the end of the 21st century, the interannual variability of mean precipitation and extreme precipitation will become even larger under SSP-585, indicating an increased risk of extreme droughts/floods in Central Asia under high greenhouse gas emissions. Our findings provide technical support for climate change impact assessments in the region and highlight the potential of CNN-based downscaling for future climate change studies. Full article