喜马拉雅山拥有全球中低纬度带规模最大的山地冰川群，其冰川补给直接影响南亚水系水资源安全。全球变暖背景下，喜马拉雅山冰川响应存在空间分异特征，但21世纪以来冰川动态演化路径及其水文效应仍存在整体上的认知空白。本研究利用偏差校正的CMIP6气候数据集与改进型Global PyGEM-OGGM模型，综合考虑冰川动力学过程与表碛热力学效应，分析预测2000—2100年SSP2-4.5和SSP5-8.5情景下喜马拉雅山冰川系统多参数响应。结果表明：(1)经偏差校正后，CMIP6多模式集合数据在喜马拉雅山的适用性显著提升(1961—2014年),气温(偏差：-0.02℃,均方根误差：0.41℃)和降水(偏差：-22.31 mm,均方根误差：136.55 mm)的模拟误差显著降低，同时基于多源卫星融合数据验证的Global PyGEM-OGGM冰川数据集(2000—2019年)在冰川质量变化时空模拟中表现优异(相关系数分别为0.59、0.99,均方根误差为0.97 Gt、0.002 Gt),证实二者可为区域气候变化与冰川物质平衡研究提供可靠数据支撑。(2)在SSP2-4.5和SSP5-8.5情景下，...

The Tibetan Plateau (TP) covers the largest regions under low- and mid-latitude permafrost. The evolution of permafrost has significantly affected the hydrology, biogeochemistry, and infrastructure of Asia. However, model reconstructions of long-term permafrost evolution with high accuracy and reliability are insufficient. Here, spatial changes in mean annual ground temperature at the depth where the annual amplitude is zero (MAGT) on the TP since 1981 were modeled and validated based on temperature records from 155 boreholes, and future changes were predicted under scenarios from the Climate Model Intercomparison Project 6 (CMIP6). The results indicated that the MAGT on the TP was approximately 1.5 degrees C (2010 - 2018), and the corresponding permafrost extent on the TP is estimated to be approximately 1.03 x 106 km2, which is projected to decrease to 0.77 x 106, 0.50 x 106, 0.30 x 106, and 0.17 x 106 km2 under the scenarios of shared socioeconomic pathway (SSP)126, SSP245, SSP370, and SSP585, respectively, by 2100. As predicted in the SSP585 scenario, permafrost is predicted to largely disappear from many basins of major Asian rivers, such as the Yarlung Zangpo-Brahmaputra, NuSalween, and Lancang-Mekong Rivers, between 2041 and 2060, followed by the Yellow and Yangtze Rivers between 2061 and 2080. Moreover, the original stable permafrost in the West Kunlun Mountains will change to transitional and unstable conditions. Our study offers comprehensive datasets of year-to-year ground temperatures and permafrost extent maps for the TP, which can serve as a fundamental resource for further investigations on the hydrogeology, engineering geology, ecology, and geochemistry of the TP.

Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 degrees C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.

Flash floods represent a significant threat, triggering severe natural disasters and leading to extensive damage to properties and infrastructure, which in turn results in the loss of lives and significant economic damages. In this study, a comprehensive statistical approach was applied to future flood predictions in the coastal basin of North Al-Abatinah, Oman. In this context, the initial step involves analyzing eighteen General Circulation Models (GCMs) to identify the most suitable one. Subsequently, we assessed four CMIP6 scenarios for future rainfall analysis. Next, different Machine Learning (ML) algorithms were employed through H2O-AutoML to identify the best model for downscaling future rainfall predictions. Forty distribution functions were then fitted to the future daily rainfall, and the best-fit model was selected to project future Intensity-Duration-Frequency (IDF) curves. Finally, the Soil and Water Assessment Tool (SWAT) model was utilized with sub-daily time steps to make accurate flash flood predictions in the study area. The findings reveal that IITM-ESM is the most effective among GCM models. Additionally, the application of stacked ensemble ML model proved to be the most reliable in downscaling future rainfall. Furthermore, this study highlighted that floods entering urbanized areas could reach 20.33 and 20.70 m(3)/s under pessimistic scenarios during rainfall events with 100 and 200-year return periods, respectively. This hierarchical comprehensive approach provides reliable results by utilizing the most effective model at each step, offering in-depth insight into future flash flood prediction.

Snow is an important factor controlling vegetation functions in high latitudes/altitudes. However, due to the lack of reliable in -situ measurements, the effects of snow on vegetation phenology remains poorly understood. Here, we examine the effects of snow cover duration (SCD) on the start of growing season (SOS) for different vegetation types. SOS and SCD were extracted from in -situ carbon flux and albedo data, respectively, at 51 eddy covariance flux sites in the northern mid -high latitudes. The effects of SCD on SOS vary substantially among different vegetation types. For grassland, preseason SCD outperforms other factors controlling grassland SOS. However, for forests and cropland, the preseason air temperature is the dominant factor in controlling SOS. Preseason SCD mainly influences the SOS by regulating preseason air and soil temperature rather than soil moisture. The CMIP6 Earth system models (ESMs) fail to capture the effect of SCD on SOS. Thus, Random Forest (RF) models were established to predict future SOS changing trends considering the effect of SCD. For grassland and evergreen needleleaf forest, the projected SOS advance rate is slower when SCD is considered. These findings can help us better understand impacts of snow on vegetation phenology and carbon -climate feedbacks in the warming world.

Permafrost degradation on the Tibetan Plateau (TP) is anticipated to result in the thaw of permafrost carbon. Existing studies have been conducted to assess the future thaw of frozen carbon on the TP, primarily focusing on the deepening of the active layer while neglecting the impact of permafrost area shrinkage. This oversight may lead to a significant underestimation of the potential thaw of frozen carbon. Our research underscores the pivotal role of permafrost area shrinkage in estimating the future thaw of frozen carbon. Our findings reveal that when the combined effects of permafrost area shrinkage and active layer deepening are considered, the thaw rates of frozen carbon in various radiative forcing scenarios are nearly four times those based on active layer deepening alone. Notably, our results demonstrate substantial thaw of frozen organic carbon in the TP permafrost area under all four future scenarios: In the low radiative forcing scenario SSP1-2.6, it is predicted that 55.4 % of the organic carbon in the permafrost area 0-10 m soils will be in a state of thaw by 2100, and more than 90 % in the high radiative forcing scenario SSP5-8.5. This substantial thaw is poised to diminish the TP's current carbon sink function significantly. Our study emphasizes that as global warming persists, frozen carbon in permafrost areas will play a more active role in global carbon cycle processes in the future. Furthermore, we stress the importance of considering permafrost area shrinkage in understanding the thaw of frozen carbon, providing valuable insights for carbon balance studies on the TP.

基于第六次国际耦合模式比较计划（CMIP6）的历史模拟试验以及情景预估试验数据，分析了21世纪中（2035—2064年）、后期（2070—2099年）青藏高原积雪相对于参考期（1985—2014年）的变化。结果表明：相对于参考期，21世纪中、后期青藏高原平均年积雪日数、平均积雪期均表现为减少，减少幅度总体随着人为辐射强迫的增加而加大；除低强迫情景外，21世纪后期的减少幅度均大于21世纪中期；空间上总体表现为青藏高原东南部的减少幅度大于西北部。21世纪中、后期青藏高原积雪初日均表现为推迟、积雪终日均表现为提前，积雪初日推迟天数是积雪终日提前天数的1.5～2.0倍；人为辐射强迫越高，积雪初（终）日推迟（提前）天数越多；相同情景下21世纪后期积雪初（终）日推迟（提前）天数均多于21世纪中期。降雪（气温）与年积雪日数呈正（负）相关；随着人为辐射强迫的增加，降雪对年积雪日数的相对贡献率总体呈增加趋势；空间特征表现为降雪（气温）对青藏高原南部和北部（东部和西部）的年积雪日数的相对贡献更大。7—12月降雪的减少幅度大于1—6月，这可能是积雪初日推迟天数多于积雪终日提前天数的重要原因。不同情景下青藏高...

Compound floods induced by co-occurring multiple drivers may exacerbate the flood impacts and lead to larger flood damage. Exploring future changes in compound flood risk is imperative for flood management and disaster reduction. This study attempts to investigate future changes in compound flood risk across the Yangtze River Basin during 2030 similar to 2100. Future river flow was projected using an improved hydrological model and pairwise series of extremes of rainfall and river flow were extracted from both observed and projected series. Subsequently, stochastic pairs of rainfall and river flow characterizing compound floods were proportionally sampled from their bivariate joint distributions. The damage from each compound flood was obtained from the flood damage function constructed by Random Forests (RF). Further, the expected annual damage (EAD) was calculated to investigate future changes in compound flood risk. Results show that: (1) Future annual maximums of rainfall and river flow are expected to increase by 14.51 % similar to 66.13 % and 1.72 % similar to 55.73 % in the mainstream and northern tributaries, while future annual peak discharge in the southern tributaries (except for the Dongting Lake Basin) is expected to decrease by 4.18 % similar to 12.30 %. A similar spatial distribution of future changes is also found in the bivariate joint distribution of rainfall and river flow. (2) The high coefficient of determination (R-2) of 0.84 indicates the satisfactory simulation and projection capacity of the constructed flood damage function. The positive stepped relationship between flood damage and rainfall or river flow reflects the superposition of multiple flood damage processes. (3) The Han River Basin, the Jialing River Basin, and the two-lakes (the Dongting and Poyang Lakes) area face great threat from compound floods in both historical and future periods. Future compound flood risk is expected to increase by 13.43 % similar to 46.04 % in these regions except for the Poyang Lake Basin, while future risk is expected to increase by 2.03 % similar to 46.04 % in the whole basin. The findings help improve the understanding of future flood risk variations in the Yangtze River Basin and provide essential information for damage reduction.

积雪是对气候变化响应最敏感的自然要素之一，对地表的辐射平衡和水循环有着重要影响，全球积雪覆盖面积约为46×10～6 km2，且98%分布在北半球，由于积雪具有独特的辐射（高表面反照率）和热（低热传导率）特性，其变化对陆地和大气之间的能量平衡和水循环过程具有重要的影响，在全球变暖背景下，近几十年来北半球积雪覆盖面积减少趋势明显，尤其春季最明显，基于观测数据评估CMIP6模式数据对于积雪覆盖面积的模拟能力，应用多模式平均评估未来时期积雪覆盖度的变化情况。本文以美国国家海洋和大气管理局/美国国家气候数据中心（NOAA/NCDC）的积雪产品为参考数据，采用泰勒技巧评分、相对偏差等方法，对国际耦合模式比较计划第六阶段（CMIP6）发布的1982-2014年北半球春季积雪覆盖度（SCF）数据进行评估，并选取排名前三的模式的集合平均预估未来（2015-2099年）不同排放情景下SCF的时空变化特征。结果表明：历史时期（1982-2014年）从整体上看，积雪覆盖度呈现出高纬高，低纬低，青藏高原和亚洲东部等高海拔地区较同纬地区高的特点，北半球的积雪覆盖度呈减少趋势地区为68.37%...

基于第六次耦合模式比较计划（CMIP6）的模式模拟数据和欧洲宇航局GlobSnow卫星遥感雪水当量（Snow Water Equivalent, SWE）资料，评估了CMIP6耦合模式对1981～2014年欧亚大陆冬季SWE的模拟能力，并应用多模式集合平均结果预估了21世纪欧亚大陆SWE的变化情况。结果表明，CMIP6耦合模式对冬季欧亚大陆中高纬度SWE空间分布具有较好的再现能力，能模拟出欧亚大陆中高纬度SWE的主要分布特征；耦合模式对SWE变化趋势及经验正交函数主要模态特征的模拟能力存在较大差异，但多模式集合能提高模式对SWE变化趋势和主要时空变化特征的模拟能力；此外，多模式集合结果对欧亚大陆冬季SWE与降水、气温的关系也有较好的再现能力。预估结果表明，21世纪欧亚大陆东北大部分地区的SWE均要高于基准期（1995～2014年），而90°E以西的欧洲大陆SWE基本上呈现减少的特征；21世纪早期，4种不同排放情景下积雪变化的差异不大，但21世纪后期积雪变化的幅度差异较大，而且排放越高积雪变化的幅度越大，模式不确定性也越大；进一步的分析表明，欧亚大陆冬季未来积雪变化特征的空间分布与全球变...