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Correctly modeling snow is critical for climate models and for hydrologic applications. Snowpack simulated by six land surface models (LSM: Noah, Variable Infiltration Capacity, snow-atmosphere-soil transfer, Land Ecosystem-Atmosphere Feedback, Noah with Multiparameterization, and Community Land Model) were evaluated against 1 year snow water equivalent (SWE) data at 112 Snow Telemetry (SNOTEL) sites in the Colorado River Headwaters region and 4 year flux tower data at two AmeriFlux sites. All models captured the main characteristics of the seasonal SWE evolution fairly well at 112 SNOTEL sites. No single model performed the best to capture the combined features of the peak SWE, the timing of peak SWE, and the length of snow season. Evaluating only simulated SWE is deceiving and does not reveal critical deficiencies in models, because the models could produce similar SWE for starkly different reasons. Sensitivity experiments revealed that the models responded differently to variations of forest coverage. The treatment of snow albedo and its cascading effects on surface energy deficit, surface temperature, stability correction, and turbulent fluxes was a major intermodel discrepancy. Six LSMs substantially overestimated (underestimated) radiative flux (heat flux), a crucial deficiency in representing winter land-atmosphere feedback in coupled weather and climate models. Results showed significant intermodel differences in snowmelt efficiency and sublimation efficiency, and models with high rate of snow accumulation and melt were able to reproduce the observed seasonal evolution of SWE. This study highlights that the parameterization of cascading effects of snow albedo and below-canopy turbulence and radiation transfer is critical not only for SWE simulation but also for correctly capturing the winter land-atmosphere interactions.

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Alpine grassland and the soil on which it is growing in the Qinghai‐Tibetan Plateau (QTP) of China is being degraded in an attempt to increase food and feed production for an increasing global population. Our objective was to use soil quality assessment to quantify changes in soil chemical and physical properties at three depth increments (0 to 4, 4 to 10, and 10 to 20 cm) and thus determine the linkages between soil and vegetation changes, the soil element(s) limiting grassland restoration in alpine region, and the ability to restore soil fertility by reestablishing grasslands. The soil and vegetation were sampled in the different types of degraded grasslands, that is, moderately degraded grassland (MDG), heavily degraded grassland (HDG) and severely degraded grassland (SDG) as well as in the reestablished grasslands at different ages, that is, 5‐yr restored grassland (5yRG), 7‐yr restored grassland (7yRG), and 9‐yr restored grassland (9yRG) for comparative study. The results show: (i) decreased water holding capacity and increased soil hardness as vegetative cover declined, (ii) decreased soil organic carbon (OC) and total nitrogen (TN) and increased total soil potassium, (TK) (iii) the establishment of artificial grassland did not restore soil quality or nutrient stocks within degraded grassland soils, and (iv) yearly variations in soil properties at different depths were significant along the degree of grassland degradation. Significant variations of soil physical and chemical parameters might be attributed to loss of the top soil and changes of vegetation composition and soil and textures. Soil quality can be used to assess grassland degradation and restoration in the alpine region. In conclusion, better soil management is needed for restoring the degraded alpine grasslands on the QTP.