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.

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERAS-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i. e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m(3)/m(3), respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m(3)/m(3), respectively) performed better than ERAS-Land (R = 0.46; ubRMSE = 0.03 m(3)/m(3)). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs.

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.

针对青藏高原植被稀疏、土壤颗粒较粗糙的特征,基于Noah陆面过程模型（LSM）,模拟了植被和土壤对整个高原多年冻土分布和关键属性特征（包括活动层厚度和年平均地温）的影响,并通过野外调查数据对模拟结果进行了评估。结果表明:在考虑稀疏植被和粗糙土壤后,改进的Noah LSM对青藏高原多年冻土分布和属性的模拟性能都有所改善;多年冻土面积由原始Noah模型模拟的1.216×10～6km2减少到1.113×10～6km2,模拟的空间差异主要出现在多年冻土与季节冻土的过渡区及高原南部的岛状多年冻土区;模拟的高原平均活动层厚度由原始Noah模型模拟的2.55 m增加到2.92 m,年平均地温也由-2.17℃增加到-1.65℃。总之,青藏高原稀疏植被和粗糙土壤对多年冻土有重要影响。

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.