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The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

An analysis is carried out for two hydrologically contrasting but thermodynamically similar areas on the Tibetan Plateau, to evaluate soil moisture analysis based on the European Centre for Medium‐Range Weather Forecasts (ECMWF) previous optimum interpolation scheme and the current point‐wise extended Kalman filter scheme. To implement the analysis, this study used two regional soil moisture and soil temperature networks (i.e., Naqu and Maqu) on the Tibetan Plateau. For the cold‐semiarid Naqu area, both ECMWF soil moisture analyses significantly overestimate the regional soil moisture in the monsoon seasons. For the cold‐humid Maqu network area, the ECMWF products have comparable accuracy as reported by previous studies in the humid monsoon period. The comparisons were made among the liquid soil moisture analysis from ECMWF, the ground station's measurements and the satellite estimates from the Advanced Scatterometer sensor. The results show reasonable performances of the ECMWF soil moisture analyses (i.e., both optimum interpolation and extended Kalman filter products) and the Advanced Scatterometer level 2 products, when compared to the in situ measurements.

Accurate representing freeze-thaw (FT) process is of great importance in cold region hydrology and climate studies. With the STEMMUS-FT model (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil), we investigated the coupled water and heat transfer in the variably saturated frozen soil and the mechanisms of water phase change along with both evaporation and FT process, at a typical meadow ecosystem on the Tibetan Plateau. The STEMMUS-FT showed its capability of depicting the simultaneous movement of soil moisture and heat flow in frozen soil. The comparison of different parameterizations of soil thermal conductivity indicated that the de Vries parameterization performed better than others in reproducing the hydrothermal dynamics of frozen soils. The analysis of water/vapor fluxes indicated that both the liquid water and vapor fluxes move upward to the freezing front and highlighted the crucial role of vapor flow during soil FT cycles as it connects the water/vapor transfer beneath the freezing front and above the evaporation front. The liquid/vapor advective fluxes make a negligible contribution to the total mass transfer. Nevertheless, the interactive effect of soil ice and air can be found on the spatial and temporal variations of advective fluxes in frozen soils.

[1] The concept of enhanced vapor transfer in unsaturated soils has been questioned for its reliance on soil temperature gradient, which leads to consideration of other mechanisms of vapor transfer, e.g., advective vapor transfer due to soil air pressure gradient. Although the advective flux is an important portion of evaporation, there is a lack of knowledge of its effect on evaporation. In order to assess the dependence of evaporation on the soil air pressure gradient, a vertical one-dimensional two-phase heat and mass flow model is developed that fully considers diffusion, advection, and dispersion. The proposed model is calibrated with field measurements of soil moisture content and temperature in the Badain Jaran Desert. The proposed model is then used to investigate the advective effect in both low-and high-permeability soils. The advective effect is reflected by underestimating evaporation when the airflow is neglected and is more evident in the low-permeability soil. Neglecting airflow causes an underestimation error of 53.3% on the day right after a rainfall event in the low-permeability soil (7.9 Â 10 À4 cm s À1) and 33.3% in the high-permeability soil (2 Â 10 À3 cm s À1). The comparisons of driving forces and conductivities show that the isothermal liquid flux, driven by the soil matric potential gradient, is the main reason for the underestimation error.Citation: Zeng, Y., Z. Su, L. Wan, and J. Wen (2011), A simulation analysis of the advective effect on evaporation using a two-phase heat and mass flow model, Water Resour.