Frozen soil, covering most of the Tibetan Plateau (TP), critically influences land surface and climate simulations. Although some studies have made advancements in simulations, further investigation into the distinct mechanisms underlying relevant parameterization schemes remains essential. This study compares two frozen soil permeability schemes in Noah-MP (NY06: high-permeability; Koren99: low-permeability) to elucidate their distinct hydrological mechanisms. Although significant disparities exist in the simulation of soil water and ice content between the two schemes in permafrost regions, the simulated soil water content in the shallow layer exhibits similarity. Their underlying physical processes behind this similarity differ fundamentally: Koren99 relies on cross-seasonal ice melt recharge, whereas NY06 depends more on current-season precipitation and snowmelt. With greater soil depth, soil water differences progressively propagate downward, amplifying variations in hydraulic conductivity, and soil memory effects become increasingly dominant. Meanwhile, the Koren99 scheme more effectively impedes bottom-up melting water transport than top-down effect. However, the aforementioned disparities are not apparent in seasonally frozen soil. Notable disparities also exist in simulated evapotranspiration and surface runoff over permafrost regions, particularly during the summer months. This research investigates the differences in water transport within frozen soil over the TP, elucidates the distinct hydrological mechanisms underlying different frozen soil permeability schemes, and highlights that similar soil hydrothermal simulations are associated with different physical processes, leading to varying degrees of effectiveness in soil memory. Furthermore, this research elucidates the dual role of soil ice (permeability restriction and water storage) in hydrological processes, providing a theoretical basis for improving frozen soil parameterization.

地表反照率是影响地–气相互作用的关键因子，而准确描述地表反照率是改进陆面模型水热模拟能力的关键。当前Noah-MP (the Noah land surface model with Multiple Parameterizations)土壤反照率估算主要依赖于查找表方法，该方法基于土壤颜色获得不同土壤类型的反照率，但在区域尺度上土壤颜色等级尚未得到有效率定，直接影响了区域反照率模拟水平。此外，裸土反照率的计算还高度依赖于土壤水分。针对这一问题，以同化得到的土壤水分数据作为输入，计算得到不同土壤颜色等级对应的反照率时间序列。在此基础上，以MODIS反照率为参照，同时排除高植被覆盖和积雪的影响，逐步筛选得到青藏高原区域0.25°格点尺度下最优的土壤颜色等级。评估结果表明，优化得到的土壤颜色等级空间分布规律符合土壤质地与反照率之间的物理规律，且改进了研究区域70%空间网格内的Noah-MP模型反照率估计。

地表反照率是影响地–气相互作用的关键因子，而准确描述地表反照率是改进陆面模型水热模拟能力的关键。当前Noah-MP (the Noah land surface model with Multiple Parameterizations)土壤反照率估算主要依赖于查找表方法，该方法基于土壤颜色获得不同土壤类型的反照率，但在区域尺度上土壤颜色等级尚未得到有效率定，直接影响了区域反照率模拟水平。此外，裸土反照率的计算还高度依赖于土壤水分。针对这一问题，以同化得到的土壤水分数据作为输入，计算得到不同土壤颜色等级对应的反照率时间序列。在此基础上，以MODIS反照率为参照，同时排除高植被覆盖和积雪的影响，逐步筛选得到青藏高原区域0.25°格点尺度下最优的土壤颜色等级。评估结果表明，优化得到的土壤颜色等级空间分布规律符合土壤质地与反照率之间的物理规律，且改进了研究区域70%空间网格内的Noah-MP模型反照率估计。

活动层内部的冻融锋面是冻融过程中冻结土层与融化土层的分界面,其上下土层的水热参数有着显著差异。在陆面过程模式中准确描述冻融锋面的移动过程将有助于提高其对多年冻土水热过程的模拟能力。本研究首先将Noah-MP陆面过程模式的模拟深度扩展到20 m,并将原模式的4层土层增加到19层土层,同时引入前人的有机质方案和植被根系方案,然后在此基础上,通过耦合Stefan方法以加强模式对冻融锋面的模拟能力,进而探究耦合Stefan方法的Noah-MP模式对西大滩多年冻土站点水热过程的模拟效果。研究中设置了不耦合Stefan方法的CTL控制试验和耦合Stefan方法的STE对照试验来分别模拟西大滩多年冻土站点2012年0～20 m的土壤温度与土壤液态含水量,模拟结果用站点0～3.2 m内10个深度的日均土壤温度、土壤液态水含量监测数据以及3 m、6 m和10 m的年均地温监测数据来做验证。研究结果表明,由土壤温度模拟值插值得到的冻融锋面（0℃等温线）有明显阶梯状特征,最大冻融深度与实测相比偏大。耦合Stefan方法增强了Noah-MP模式模拟冻融锋面的能力,使得模式能够基于Stefan方法较好地模拟出冻...

活动层内部的冻融锋面是冻融过程中冻结土层与融化土层的分界面,其上下土层的水热参数有着显著差异。在陆面过程模式中准确描述冻融锋面的移动过程将有助于提高其对多年冻土水热过程的模拟能力。本研究首先将Noah-MP陆面过程模式的模拟深度扩展到20 m,并将原模式的4层土层增加到19层土层,同时引入前人的有机质方案和植被根系方案,然后在此基础上,通过耦合Stefan方法以加强模式对冻融锋面的模拟能力,进而探究耦合Stefan方法的Noah-MP模式对西大滩多年冻土站点水热过程的模拟效果。研究中设置了不耦合Stefan方法的CTL控制试验和耦合Stefan方法的STE对照试验来分别模拟西大滩多年冻土站点2012年0～20 m的土壤温度与土壤液态含水量,模拟结果用站点0～3.2 m内10个深度的日均土壤温度、土壤液态水含量监测数据以及3 m、6 m和10 m的年均地温监测数据来做验证。研究结果表明,由土壤温度模拟值插值得到的冻融锋面（0℃等温线）有明显阶梯状特征,最大冻融深度与实测相比偏大。耦合Stefan方法增强了Noah-MP模式模拟冻融锋面的能力,使得模式能够基于Stefan方法较好地模拟出冻...

Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are -0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

为研究不同陆面模式对中国区域土壤温度的模拟效果，基于中国气象局陆面数据同化系统（CMA Land Data Assimilation System, CLDAS）大气驱动数据分别驱动Noah和Noah-MP陆面模式进行中国区域土壤温度的模拟（简称：CLDAS＿Noah和CLDAS＿Noah-MP试验），使用2010—2018年中国气象局2380个土壤温度观测站点10和40 cm观测数据以及美国全球陆面数据同化系统（The Global Land Data Assimilation System,GLDAS）驱动的Noah模式（GLDAS＿Noah试验）模拟的土壤温度结果，从空间分布、季节、分区等角度进行了评估，实现了不同驱动数据相同陆面模式和相同驱动数据不同陆面模式的对比分析。结果表明：GLDAS＿Noah、CLDAS＿Noah和CLDAS＿Noah-MP试验均能合理模拟出中国区域土壤温度空间分布，但在量级上有一定差异，主要表现在中国东北、新疆、青藏高原等积雪区。对于相同陆面模式不同驱动数据，均方根误差显示CLDAS＿Noah试验在季节与分区上均优于GLDAS＿Noah试验，间接表明C...

为研究不同陆面模式对中国区域土壤温度的模拟效果，基于中国气象局陆面数据同化系统（CMA Land Data Assimilation System, CLDAS）大气驱动数据分别驱动Noah和Noah-MP陆面模式进行中国区域土壤温度的模拟（简称：CLDAS＿Noah和CLDAS＿Noah-MP试验），使用2010—2018年中国气象局2380个土壤温度观测站点10和40 cm观测数据以及美国全球陆面数据同化系统（The Global Land Data Assimilation System,GLDAS）驱动的Noah模式（GLDAS＿Noah试验）模拟的土壤温度结果，从空间分布、季节、分区等角度进行了评估，实现了不同驱动数据相同陆面模式和相同驱动数据不同陆面模式的对比分析。结果表明：GLDAS＿Noah、CLDAS＿Noah和CLDAS＿Noah-MP试验均能合理模拟出中国区域土壤温度空间分布，但在量级上有一定差异，主要表现在中国东北、新疆、青藏高原等积雪区。对于相同陆面模式不同驱动数据，均方根误差显示CLDAS＿Noah试验在季节与分区上均优于GLDAS＿Noah试验，间接表明C...

Soil hydrothermal regime of the active layer in the permafrost regions of the Qinghai-Tibet Plateau (QTP) is important to the underlying permafrost and the climate change dynamics in Asia. However, a large bias still exists in current land surface models in the representation of soil temperature and moisture. This study assessed and augmented the Noah land surface model with multiparameterization options (Noah-MP) for simulating soil hydrothermal dynamics at the Tanggula (alpine meadow) and Beiluhe (alpine swamp) stations located in the permafrost regions of the QTP. The results showed that the default Noah-MP tended to underestimate soil temperature and moisture. Specifically, the default model overestimated the snow depth and duration due to the low snow sublimation rate. This resulted in a cold deviation in the soil temperature at two stations. Such underestimation was reduced by introducing a scheme that considered the sublimation loss from wind. Moreover, the remaining cold bias in the soil profiles of two stations was greatly resolved by a combined scheme of roughness length for heat (Z(0h)) and undercanopy aerodynamic resistance (r(a,g)). A soil thermal conductivity scheme, which can produce more realistic soil thermal conductivity in frozen soil, further improved the deep soil temperature simulation. The consideration of soil organic matter could mitigate the underestimation of the shallow soil moisture to some extent, but this improvement was more obvious at the Tanggula station, which had coarser mineral soil than the Beiluhe station.

Wind erosion along the Qinghai-Tibet Railway causes sand hazard and poses threats to the safety of trains and passengers. A coupled land-surface erosion model (Noah-MPWE) was developed to simulate the wind erosion along the railway. Comparison with the data from the Cs-137 isotope analysis shows that this coupled model could simulate the mean erosion amount reasonably. The coupled model was then applied to eight sites along the railway to investigate the wind-erosion distribution and variations from 1979 to 2012. Factors affecting wind erosion spatially and temporally were assessed as well. Majority wind erosion occurs in the non-monsoon season from December to April of the next year except for the site located in desert. The region between Wudaoliang and Tanggula has higher wind erosion occurrences and soil lose amount because of higher frequency of strong wind and relatively lower soil moisture than other sites. Inter-annually, all sites present a significant decreasing trend of annual soil loss with an average rate of - 0.18 kg m(-2) a(-1) in 1979-2012. Decreased frequency of strong wind, increased precipitation and soil moisture contribute to the reduction of wind erosion in 1979-2012. Snow cover duration and vegetation coverage also have great impact on the occurrence of wind erosion.