The Tibetan Plateau, known as the Earth's largest and highest plateau, functions as a crucial nexus for global atmospheric processes. It exerts a pivotal influence on the hydroclimate dynamics of East Asia. Nevertheless, achieving a comprehensive understanding of historical and recent hydroclimate variations, along with their far-reaching ecological and societal impacts, has proven to be a formidable challenge due to limited observational data and uncertainties in proxy reconstructions. In this study, we have reconstructed the precipitation changes in the eastern Tibetan Plateau over the past 2000 years based on tree-ring δ¹⁸O data. This reconstruction emerges as a reliable proxy for precipitation changes in the central and eastern regions of China. Further research found that our precipitation reconstruction of the Tibetan Plateau unveils coherent variations between the Asian monsoon and the Westerlies.

How to cite: Liu, Y.: Hydroclimate variations on the Tibetan Plateau over the past 2000 years, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7563, https://doi.org/10.5194/egusphere-egu25-7563, 2025.

The Tibetan Plateau (TP) is also known as the ‘Water Tower of Asia’ as it is the source of 10 major rivers. Significant changes in the natural and social environment of the TP have occurred over the last 50 years (e.g., temperatures have warmed twice as much as the global average over the same period), and there is considerable uncertainty about future environmental change. Water vapor flux, expressed as evapotranspiration (ET), is crucial for understanding the water balance over the TP. The TP is rich in land cover types, including grasslands, deserts, lakes, forests, glaciers, snow, etc. The dynamics and thermodynamics of the subsurface vary greatly between different climate types, making it a major challenge to conduct large-scale studies of ET processes over the TP and to explore the governing mechanisms and feedbacks to the climate system and hydrological processes. However, a single ET dataset cannot provide a reliable answer on how much water is evaporated from the TP due to model limitations in describing complete processes and uncertainties in different datasets. In this study, we first evaluated 22 ET products in the TP against in-situ observations and basin-scale water balance estimates. The spatiotemporal variability of the total vapor flux was also evaluated to clarify the vapor flux magnitude and variability over the TP. The results showed that the high-resolution (~1km) global ET data based on observations from ETMonitor and PMLV2 were more accurate than than other global and regional ET data with fine spatial resolution (~1km), when comparing with in-situ observations. When compared with basin scale water balance estimates of ET, ETMonitor and PMLV2 at finer spatial resolution and GLEAM and TerraClimate at the coarse spatial resolution showed good agreement. Different products showed different patterns of spatiotemporal variability, with large differences in the central to western TP. The mean water vapor flux over multi-year and multi-product over the TP was 333.1 mm/yr with a standard deviation of 38.3 mm/yr. Soil evaporation accounts for most of the total water vapor flux over the TP, followed by plant transpiration and canopy rainfall interception evaporation, while the contributions from open water evaporation and snow/ice sublimation are not negligible.

How to cite: Jia, L. and Zheng, C.: Assessing how much water evaporated from the Tibetan Plateau using multiple datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19923, https://doi.org/10.5194/egusphere-egu25-19923, 2025.

Permafrost degradation on the Qinghai - Tibet Plateau (QTP) have a direct impact on the evapotranspiration and moisture recycling process by changing underlying land surface conditions. Up to now, the contribution of thermokarst lake evaporation and plant transpiration to local precipitation in the permafrost regions of the QTP remains unknown. This study collected precipitation, thermokarst lake water, plant, and soil samples, and quantitatively estimated the proportional contributions of thermokarst lake evaporation, soil evaporation, and plant transpiration to local precipitation by the Bayesian isotopic mixing model in the permafrost region of central QTP. Results showed that the contribution of advection vapor to local precipitation was dominant, with a mean value of 74.6 %, and the moisture recycling ratio ranged from 19.7 ± 2.1 % to 29.7 ± 3.6 % (mean: 25.4 %). The mean contribution fraction of thermokarst lake evaporation and soil evaporation were 9.2 % and 8.9 %, respectively. Contrary to the findings of related studies in the nearby Qilian Mountains and arid central Asian oases, the total surface evaporation contribution of 18.1% was considerably higher than the 7.4% of plant transpiration in this study area, which was attributed to the rapid expansion of thermokarst lakes, sparse vegetation, and higher soil moisture condition in the shallow active layer.

How to cite: Wu, T. and Zhu, X.: Larger contribution of evaporation from soil and thermokarst lake to local precipitation than plant transpiration in the permafrost regions of central Qinghai-Tibet Plateau, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14353, https://doi.org/10.5194/egusphere-egu25-14353, 2025.

The Yarlung Zsangbo Grand Canyon (YGC) is one of the world's deepest canyons. In this sparsely gauged region, remotely sensed precipitation products can be valuable. A new rain gauge network was installed in the YGC in November 2018, and the observations were utilized to evaluate and calibrate the Integrated Multi‐satellite Retrieval for Global Precipitation Measurements (IMERG) precipitation product. The evaluation results demonstrate that the IMERG data reasonably captured the observed seasonal and diurnal variations in the precipitation but with much weaker seasonal and diurnal variations. IMERG underestimated the total rainfall primarily due to under-detection of rainfall events, with misses being more prevalent than false alarms. IMERG overestimated and underestimated the light and heavy precipitation, respectively, leading to a significant underestimation of the rainfall frequency and intensity at both the daily and monthly scales. The probability of detection decreased with elevation, leading to increased underestimation of rainfall events at higher elevations, and the false alarm ratio was higher in valley sites. In terms of the hit events, IMERG overestimated the light rainfall events and underestimated the heavy rainfall events and the negative bias in the hit events decreased with elevation. The GPCC calibration partially improved the underestimation of GPM, but not sufficient. This study fills the gap in IMERG validation in a complex mountainous region and has implications for users and developers.

How to cite: Chen, X.: Evaluation of the GPM IMERG product in the Yarlung Zsangbo Grand Canyon of the eastern Himalaya, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1252, https://doi.org/10.5194/egusphere-egu25-1252, 2025.

Significant progress has been made in understanding the relationship between Tibetan Plateau (TP) summer precipitation and the summer North Atlantic Oscillation (SNAO). However, the role of topography on this relationship remains unclear. The central-eastern Himalayas (CEH), a key high-altitude barrier on the southern edge of the TP, experiences concentrated summer rainfall and is a crucial water source. Analysis of long-term observations and reanalysis data revealed that the SNAO-driven positive summer precipitation in the CEH was influenced more by topographic mechanical forcing than by the impacts of atmospheric circulation. Topography forces horizontal winds to generate a strong climb flow component, driving changes in the precipitation distribution. Experiments removing topographic features show that the original positive precipitation distribution shifts into a dipole-like pattern, dominated by negative distribution, which are directly governed by atmospheric circulation. Thus, accurate predictions of future summer precipitation in the CEH should consider both dynamic topographic and atmospheric processes.

How to cite: Zhang, Q., Chen, X., and Ma, Y.: Topographic Influence on SNAO-Driven Summer Precipitation Variability in the Central-Eastern Himalayas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5931, https://doi.org/10.5194/egusphere-egu25-5931, 2025.

The Tibetan Plateau, as the source of major rivers in Asia, plays a critical role in influencing the ecological environment, production, and livelihoods both within the region and downstream. Consequently, enhancing the accuracy and efficiency of extreme weather forecasting in this area is of paramount importance. This study introduces a lightning data assimilation scheme that utilizes the Fengyun-4A Lightning Mapping Imager and the Advanced Direction Time Lightning Detection System (ADTD) flash extent density to assign a pseudo-relative humidity between the cloud base and a specific atmospheric layer. This study evaluates the performance of pseudo-relative humidity assimilation for short-term severe weather forecasting over the central-eastern Tibetan Plateau. The high impact severe weather event that occurred in Datong, Qinghai on 18 August 2022 is used as a case study to compare the effectiveness of lightning data assimilation with assimilation of ground, sounding and radar data for forecasting deep convection in Tibetan Plateau.

How to cite: An, J., Chen, S., and Hu, Z.: Assimilating FY-4A Lightning and Radar Data for Improving Forecasts of a High-Impact Convective Event in the Central-Eastern Tibetan Plateau, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7606, https://doi.org/10.5194/egusphere-egu25-7606, 2025.

In this study, fifth major global reanalysis produced by ECMWF (ERA5) reanalysis data from 1979 to 2022 were utilized to investigate extreme precipitation in the central Asian high mountain (CAHM) region, comprising the Pamir Plateau and western, central, and eastern Tianshan regions. This study found that westerlies and monsoons are the primary drivers of extreme precipitation, with distinct mechanisms in the southwestern and northeastern CAHM (divided at approximately 79^oE). In the southwestern CAHM, a weak Indian summer monsoon (ISM) leads to negative potential height anomalies, enhancing meridional water vapor flux from the Bay of Bengal and Arabian Sea, thereby increasing precipitation. Conversely, extreme precipitation is associated with the negative phase of the Silk Road pattern in the northeastern CAHM. While the East Asian summer monsoon (EASM) plays a lesser role, it influences water vapor supplies and atmospheric circulation in the southwestern CAHM and modulate meridional wind position in the northeastern CAHM with the ISM, contributing to extreme precipitation. Seasonal analysis revealed May as the peak for extreme precipitation in the southwestern CAHM region, while extreme precipitation in the northeastern CAHM region peaked in the midmonsoon months (June and July) due to the synergy between monsoons and westerlies of different strengths passing through the CAHM.

How to cite: Zhao, W., Ma, Y., Takemi, T., Chen, X., and Cao, D.: Investigating the Underlying Mechanisms of Monsoon Season Heavy Precipitation in Central Asian High Mountain Areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14093, https://doi.org/10.5194/egusphere-egu25-14093, 2025.

The Tibetan Plateau is renowned for its complex topography and heterogeneous surface, which lead to uneven surface heating and, consequently, trigger various local circulation phenomena such as valley winds, glacier winds, and lake-land breezes. To more accurately capture these unique meteorological conditions and improve wind field simulations in complex terrain, this study introduced and compared six different boundary layer parameterization schemes to evaluate their performance in simulating the wind field in the regions of the Lake Nam Co and Mount Everest. The study utilized two three-dimensional boundary layer schemes: SMS-3DTKE and PBL3D; two variations of SMS-3DTKE—one with simplified horizontal diffusion (3DTKE_smag) and another with the horizontal diffusion term completely removed (3DTKE_0); and two traditional one-dimensional boundary layer schemes: MYNN and Shin-Hong. Observed data was used to validate the simulation results. The results indicated that the two three-dimensional boundary layer schemes provided wind profiles at the Qomolangma Atmospheric and Environmental Observation and Research Station, CAS (QOMS) that closely matched observations, significantly outperforming the one-dimensional schemes. In particular, the three-dimensional schemes not only successfully simulated the near-surface wind field and the wind characteristics at 500 meters, but also explained the mechanism behind the afternoon strong winds—caused by the convergence of westerly winds crossing the ridge and southwesterly winds in the Rongbuk Valley. Furthermore, the SMS-3DTKE scheme excelled in simulating the onset time and intensity of the lake breezes at the Nam Co Monitoring and Research Station for Multisphere Interactions, CAS (NAMORS), underscoring the importance of incorporating horizontal diffusion terms in local circulation simulations. These findings are crucial for improving wind field simulation accuracy under complex terrain conditions using three-dimensional boundary layer schemes and provide valuable insights for future research.

How to cite: Xu, H., Han, C., and Ma, Y.: Application of Three-Dimensional Boundary Layer Schemes in Wind Field Simulation under Complex Terrain of the Tibetan Plateau, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11626, https://doi.org/10.5194/egusphere-egu25-11626, 2025.

The southern Himalayan slopes are increasingly experiencing dry winters, posing significant challenges to water resources and agriculture. These conditions have intensified forest fires in Nepal, mainly driven by human activities such as unattended campfires, discarded cigarettes, and arson. A warmer and drier climate exacerbates the spread of these fires, causing severe air pollution and accelerating snow and glacier melt due to black carbon deposition in the Himalayas. Recent forest fire events, particularly in 2021 and 2024, were ten times higher than the long-term average, fueled by extended post-monsoon droughts. Climate models (CMIP3, CMIP5, and CMIP6) project worsening winter droughts, leading to an increased frequency and intensity of forest fires throughout the 21st century. Our study, based on observational, remote sensing, and climate model data, highlights climate variability and climate change-induced droughts as primary drivers of these fires. Projections indicate that persistent droughts will elevate wildfire risks and degrade air quality, posing severe threats to public health, ecosystems, and the economy. This presentation will discuss historical trends and future projections of forest fires, emphasizing their impact on regional air quality, as fire smoke can travel hundreds of kilometers.

How to cite: Aryal, D. and Pokharel, B.: Climate Change-Induced Drought and Its Role in Nepal's Forest Fires, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4621, https://doi.org/10.5194/egusphere-egu25-4621, 2025.

78% of the Three River Source Region (TRSR) is covered by grassland, understanding future variations in grassland growth is fundamental to ecological barrier security. Many advanced land surface models have incorporated vegetation growth modules, but rarely have current land surface models considered the differential growth of pasture and toxic weeds, which have quite different roles in altering surface energy and water cycles. To represent the different growth for pasture and toxic weeds, we performed a global parameter sensitivity analysis for the Community Land Model (CLM5) based on the eFAST algorithm and calibrated two sets of parameters representing pasture and toxic weeds growth. The previous overestimation of Leaf Area Index and above ground biomass was largely reduced after the parameter optimization. Then we performed future simulations for four Shared Social-economic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) during 2015-2100, considering only meteorological impacts and ignoring other future changes (e.g. CO2 or nitrogen deposition). Pasture and toxic weeds biomass (fresh weight) showed a statistically significant increasing trend in all SSPs. This trend was higher for pasture (5.58-22.76 kg·Ha^-1·yr^-1) than for toxic weeds (2.12-7.44 kg·Ha^-1·yr^-1), while toxic weeds showed greater interannual variability. Radiation and soil mineral nitrogen became the two main constraints on future grassland greening rather than warming and moisture. The strongest biomass increases in SSP5-8.5 were mainly due to increases in pasture and toxic weeds biomass in the western TRSR. Such spatial differences between indicated that the western TRSR had much greater uncertainties in the future.

How to cite: Lu, Y., Meng, X., Li, J., Liu, X., Wang, L., Deng, M., Yang, Y., Liu, B., and Yu, H.: Parameter optimizations and future projections of pasture and toxic weeds with the Community Land Model (CLM5) over the Three River Source Region, Tibetan Plateau, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2413, https://doi.org/10.5194/egusphere-egu25-2413, 2025.

The Central Himalaya faces significant air pollution challenges, with nearly half of the days each year in Kathmandu Valley surpassing the PM2.5 national air quality guideline of 40 µg/m³. Wildfire smoke, especially during the pre-monsoon season, is a major contributor to these polluted days in the valley and is also a key driver of air pollution in high-altitude regions of the Himalayas. To identify the presence of wildfire smoke in the valley, we utilized multiple datasets, including in-situ observations, Himawari satellite Aerosol Optical Depth (AOD) data, and satellite imagery. Between 2018 and 2023, we identified 114 days that met our criteria for being classified as smoke days during the pre-monsoon season. Due to the lack of in-situ observation data prior to 2018, we utilized PM2.5 data from the Copernicus Atmosphere Monitoring Service (CAMS), MODIS AOD and wildfire data to classify smoke days for earlier years. Using in total of nine variables, we trained a Random Forest Classifier model on the previously categorized dataset, our model performed with outstanding accuracy (0.91), where AOD in nearby regions (~150km) was found to be the most significant parameter, followed by number of wildfires occured in the past three days. In total, 213 days were classified as wildfire smoke days in Kathmandu Valley from 2003 to 2023, with 2021 recording the highest number of smoke days and 2009 with the highest amout of PM2.5 due to the smoke. Additionally, these wildfire smoke days do not folow any trend but were strongly correlated with wildfire occurrences in nearby regions. The Machine Learning model further highlighted the high correlation between wildfire numbers in surrounding areas and the presence of high air pollution in the valley. This research contributes to policymaking on air pollution and enhances preparedness for extreme pollution events in Kathmandu Valley, ultimately helping to protect public health and well-being.

How to cite: Kuikel, S., Sapkota, S., Kuinkel, D., Paudel, H. K., Joshi, K. P., Marahatta, S., Aryal, D., and Pokharel, B.: Prediction Air Polution due to Wildfire in Kathmandu Valley: Remote Sensing and Machine Learning Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-490, https://doi.org/10.5194/egusphere-egu25-490, 2025.

The Tibetan Plateau region is experiencing increasing runoff and sediment load in its headwater regions driven by the impacts of climate change, such as glacial retreat, permafrost degradation, and alterations in precipitation patterns. However, study on the changes in sediment load in the high-altitude Himalayas region remains challenging due to sparse observation data under the harsh climatic and topographic conditions. Recently, remote sensing has emerged as a promising tool in sediment studies, with several applications in the Himalayas; however, high cloud coverage during the high-flow season often leads to underestimation of sediment load. To address this issue, we introduce a remote-sensing approach to supplement the hydrological model calibration process using a less suspended sediment concentration (SSC) to quantify the long-term sediment transport in the Koshi River Basin (KRB). Landsat 8-9 OLI and the Landsat 4-5 TM images were selected to estimate Landsat-SSC with observed SSC data taken from the Chatara gauging station. Then the SWAT model was calibrated using the Landsat-SSC and validated by applying the monthly observed data from both the Chatara and Mulghat gauging stations. After model calibration and validation, the sediment load was simulated for 42 years (1981–2022). Additionally, the partial least squares-structural equation model (PLS-SEM) was used to quantify the complex relationships of sediment regimes with the potential influencing factors including hydro-climatic conditions, topographic variables, vegetation cover, and soil types. Results show that the surface reflectance of visible band combinations (R+G-B) exhibited the highest Pearson correlation with observed SSC data, allowing a power regression equation to estimate SSC from 1987 to 2022. The statistical analysis demonstrates a strong agreement between SWAT-SSC, Landsat-SSC, and Observed-SSC during calibration and validation. The annual sediment load of KRB at Chatara station is estimated at 75 Million tons (Mt) with a significant contribution during the monsoon season. The basin scale sediment load shows a significant increasing trend (p<0.01), with an average rate of 6.97 Mt/10a, which became more pronounced after 2001. PLS-SEM analysis shows that the above-considered potential influencing factors can explain 72% of the total variations, with a significant impact of hydro-climatic conditions (β=0.86, p<0.01) and vegetation cover (β=-0.56, p<0.05). The increasing sediment load in the KRB is primarily due to the strong influence of hydro-climatic changes. The negative influence of land cover changes highlights the buffering effect of increased vegetation cover on sediment export. Above all, by integrating remote sensing with hydrological modeling, this study applied new methods to estimate sediment loads with limited data and subsequently obtained critical insights into the impact of climatic and environmental changes on sediment transport, offering valuable information for soil conservation planning in the data-scarce Himalayan region.

How to cite: Bishwakarma, K., Xiang, Y., and Zeng, C.: Integration of remote sensing and hydrological modeling to estimate a basin-scale sediment transport in the data-scarce Himalaya region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3134, https://doi.org/10.5194/egusphere-egu25-3134, 2025.

The summer atmospheric heat source (AHS) over the Tibetan Plateau (TP) induces meridional circulations in TP and its surrounding areas. Previous studies mainly focused on the monsoon circulation on the south side of TP, while the formation and maintaining mechanism of meridional circulation on its north side remain unclear. This study compared three calculation methods of the AHS, analyzed spatial-temporal variability of the summer AHS over the TP, and discussed its influence on interannual variability of meridional circulation on the north side of TP based on the two-dimensional decomposition method of atmospheric circulation and sensitivity experiments. The results indicate that in the positive AHS anomalies years, the diabatic heating of condensation latent release in southeastern TP could motivate anomalous ascending motion. Simultaneously, the increased meridional temperature gradient between the middle and high latitudes of East Asia leads to an enhanced southward westerly jet. In this context, the region on the north side of TP, located on the north side of westerly jet entrance, is affected by negative anomalous relative vorticity advection, prevailing anomalous descending motion, which makes the descending branch of meridional circulation significantly presented. Unlike previous studies considered the descending branch of meridional circulation as the compensation for upward flow, the results of LBM model verify the descending branch is mainly influenced by the vorticity advection related to regional scale variability of westerly jet. This study reveals the physical mechanism of meridional circulation on the north side of TP, which offers valuable implications for seasonal forecasting in TP and Northwest China.

How to cite: Luo, H. and Yu, H.: Impact of the summer atmospheric heat source over the Tibetan Plateau on interannual variability of meridional circulation on the north side of Tibetan Plateau, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2423, https://doi.org/10.5194/egusphere-egu25-2423, 2025.

Lake thermal stratification is of great importance to hydrodynamics and transport of nutrients, oxygen, and primary production, which influence limnology and local climate. The thermal regime of the lakes over Tibetan Plateau (TP) was summarized as follow. During summer, solar radiation unevenly heats the water column in the vertical direction, resulting in a stratified thermal structure. The stratification dissipates in October, after which time a more uniform vertical distribution of temperature is observed. This occurs because the increased temperature gradient between the air and lake surface, combined with strong winds, drives considerable energy transfer from the lakes to the overlying air, and leads to a rapid decrease in surface water temperature. This result increases in density of the upper layers and then drives vertical convection that deepens the mixed layer. When the lakes have been completely frozen, the vertical water circulation stops; weak thermal stratification then develops and persists during winter. However, lake water near the surface warms rapidly, and rest water layer does not change much when lakes are covered with ice. When the lake ice disappears, wind-driven turbulence develops and promotes lake vertical mixing. Due to sparse observation, the lake modeling was an alternative method to simulate the seasonal lake thermal change induced by local climate change. Using lake model, the seasonal variation and magnitude of water temperature at different layers were reproduced fundamentally. The interaction heat flux and water exchange with overlying air also were simulated with reasonable error. Both the simulation and observation have shown that the thermal characteristic and ice phenology has altered: warming water and shorter ice duration, impacted by climate change. Meanwhile, the future projection of thermal response of lakes over TP to climate change shows that remarkable water temperature increase and winter ice loss which indicates less mixing frequent and shifting mixing regime. The Lake mixing events can channel the epilimnion and hypolimnion and release large amounts of potent greenhouse gases into the upper surface layer and the atmosphere in autumn, making lakes generally being considered as a weak net carbon source. The epilimnion depth show significant implications on the algal distribution, photosynthesis rates and establishing food web basis. Thus, the seasonal variations of thermal stratification and mixing in lakes can influence the aerobic life, prevent anoxia and impact on local climate and it is one of the most important factors in limnology and climate change.

How to cite: La, Z., Ma, X., Zhou, X., Yi, W., and Gan, H.: Lake thermal dynamics and its potential impact on lake ecological system on the Tibetan Plateau, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8216, https://doi.org/10.5194/egusphere-egu25-8216, 2025.

Abstract. The Qinghai-Xizang Plateau has owned a large number of various types of wetlands, which serve as major pastures for regional animal husbandry and water restoration. Among, Mitika Wetland (MW) is located in Lhari county in Nagqu city, southwest China's Xizang Autonomous Region, with an average elevation of 4,900 m.a.s.l.. It is known as the Mitika Wetland National Nature Reserve (MWNNR) in China and it is the first wetland in Xizang that has been included on the List of Wetlands of International Importance. MWNNR is also known as the headwater region of Lhasa River, the “mother river” of people in Lhasa city. Study on its current status of soil fertility and geochemical characterization is therefore crucial for better understanding of its future vagaries under the global changes.In this study, analysis of 10 soil physiochemical parameters and 37 elements were carried out for the evaluation of topsoil fertility and geochemical features of the MW. The non-parametric test results indicated that the contents of soil organic matter, total organic carbon (TOC), cation exchange capacity (CEC), soil moisture, soil bulk density and soil salinity were in large extent related to the soil type. In contrast, pH, contents of available potassium, available phosphorus, ammonia nitrogen were regardless with soil type. Comprehensive soil fertility coefficient (F) among the different soil types studied in the wetland was as following: alpine meadow soils (1.72)＞alpine shrub meadow soils (1.66)＝frigid desert soils (1.66)＞bog soils (1.56)＞skeletal soils (1.43). Except the alpine meadow soil belonged to fertility grade, the other types belonged to the general grade, as this is the common state on the Plateau. Despite the high elevation and hash climate, nevertheless, soil fertility level in MW is comparable to that of cultivated land or artificial afforestation areas in the lower altitude (<3000 m.a.s.l.) regions of Xizang. Combined results from comparison with reginal background value and source identification using multivariable analysis showed that the contents of most studied soil elements were equivalent to the background values, and their distribution have been greatly affected by the geological background. In general, this study shows that the soil fertility of the alpine wetland located in the remote northeastern Xizang with a high elevation, surprisingly, have an adequate supply of soil nutrients to the pastures and, in a large extent, still remain chemically undisturbed under global change and human activities. Obviously, the wetland has played a key role in ecologically secure of the Lhasa River catchments.

Keywords: Alpine Wetland, Ecologically Secure, Element’s background value, Source Identification, Multivariable Analysis

How to cite: Huang, X., Chen, H., and Ciren, Z.: Current status of soil fertility and geochemical characteristics of a typical alpine wetland in Xizang, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11158, https://doi.org/10.5194/egusphere-egu25-11158, 2025.

The Tibetan Plateau and surroundings, commonly referred to as the Third Pole region, has the largest ice store outside the Arctic and Antarctic regions. Glacial lakes in the Third Pole region are expanding rapidly as glaciers thin and retreat. The Landsat satellite series is the most popular for mapping glacial lakes, benefiting from long term archived data and suitable spatial resolution (30m since ~1990). However, the homogeneous mapping of high-quality, large-scale, and multi temporal glacial lake inventories using Landsat imagery relies heavily on visual inspection and manual editing due to mountain shadows, wet ice, frozen lakes, and snow cover on lake boundaries, which is time consuming and labour-intensive. Deep learning methods have been applied to glacial lake extraction in the Third Pole and other regions, yet these methods are either concentrated on small test sites without large-scale applications or in polar regions. In this study, several classical deep convolutional neural networks were evaluated, and the DeepLabv3+ with Mobilenetv3 backbone performed best, with a high accuracy of mean intersection over union (mIoU) of 94.8 % and a low loss error of 0.4 %. The proposed method demonstrated robustness in challenging conditions such as mountain shadows, frozen or partially frozen lakes, wet ice and river contact, all without requiring extensive manual correction. Compared with manual delineation, the model’s prediction has a precision rate of 86 %, recall rate of 85 %, and F1-score of 85 %. The area extracted by the model shows a strong correlation with the manual delineation (r² = 0.97, slope = 0.94) and a high intersection over union (IoU > 0.8) of the predicted areas. A test of large-scale glacial lake mapping based on the developed automated model in 2020 across the Third Pole region shows the robust performance with 29,429 glacial lakes larger than 0.0054 km² with a total area of ~1779.9 km² (including non-glacier-fed lakes). The model trained in this study can be fine-tuned for large-scale mapping of glacial lakes in other mountain regions worldwide.

How to cite: Tang, Q., Zhang, G., Yao, T., Wieland, M., Liu, L., and Kaushik, S.: Automatic extraction of glacial lakes from Landsat imagery using deep learning across the Third Pole region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1927, https://doi.org/10.5194/egusphere-egu25-1927, 2025.