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Summary Soil moisture is a key variable in climate system, and its accurate simulation needs effective soil parameter values. Conventional approaches may obtain soil parameter values at point scale, but they are costly and not efficient at grid scale (10–100 km) of current climate models. This study explores the possibility to estimate soil parameter values by assimilating AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) brightness temperature (TB) data. In the assimilation system, the TB is simulated by the coupled system of a land surface model (LSM) and a radiative transfer model (RTM), and the simulation errors highly depend on parameters in both the LSM and the RTM. Thus, sensitive soil parameters may be inversely estimated through minimizing the TB errors. A crucial step for the parameter estimation is made to suppress the contamination of uncertainties in atmospheric forcing data. The effectiveness of the estimated parameter values is evaluated against intensive measurements of soil parameters and soil moisture in three grasslands of the Tibetan Plateau and the Mongolian Plateau. The results indicate that this satellite data-based approach can improve the data quality of soil porosity, a key parameter for soil moisture modeling, and LSM simulations with the estimated parameter values reasonably reproduce the measured soil moisture. This demonstrates it is feasible to calibrate LSMs for soil moisture simulations at grid scale by assimilating microwave satellite data, although more efforts are expected to improve the robustness of the model calibration. [ABSTRACT FROM AUTHOR]

Summary Soil moisture is a key factor in energy and water cycles. Many satellite missions have been planned and implemented for retrieving soil moisture globally. Because the spatial representativeness of a point-scale soil moisture station is rather limited, a station network needs setting up for scale-matching validation of satellite-based soil moisture products. Even so, an upscaling procedure is needed to upscale these station soil moisture values into area-wide one. However, such a procedure itself introduces uncertainties into the upscaled soil moisture. In this study, four upscaling methods (simple average, block kriging, model-based, and apparent-thermal-inertia-based) are inter-compared according to their performance stability for evaluation of soil moisture estimated by assimilating microwave signals into a land surface model. It is found that the performance of the model-based upscaling approach is the most unstable because model simulations are full of uncertainties for representing spatial variability of soil moisture. The block kriging upscaling method performs not worse than the simple averaging approach; the former may generate more representative soil moisture if the range of the soil moisture semivariogram used in the block kriging is comparable to the extent of a satellite footprint. The apparent-thermal-inertia-based upscaling is the most stable one, which has been developed with the aid of high-resolution satellite thermal data. All analyses indicate that choosing a suitable upscaling approach is important for the effective evaluation of satellite-based soil moisture. Otherwise, uncertainties hiding in the upscaling method will be incorrectly attributed to errors in satellite products, undermining our confidence in implementing them into practice. [ABSTRACT FROM AUTHOR]

Highlights: [•] Precipitation types highly depend on surface air temperature, relative humidity and elevation. [•] A dynamic temperature threshold scheme is developed to discriminate the rain, sleet, and snow. [•] The new parameterization scheme outperforms 11 schemes that have been used in hydrological and land surface models. [ABSTRACT FROM AUTHOR]

Summary: A mesoscale Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN) has been established to study large-scale soil–vegetation–atmosphere interactions and to validate satellite soil moisture products. Soil moisture at four layers (0–5cm, 10cm, 20cm, and 40cm) of 30 sites was monitored since July, 2010. This paper firstly introduces the network and then presents preliminary spatiotemporal analyses based on the in situ soil moisture measurements in SMTMN. Three temporal scales (half-hourly, daily, and 10-days) and three time periods corresponding to typical soil wetness conditions, including frozen soil in winter times, are discussed. Primary findings are: (a) generally 13 randomly distributed sites in the study domain are required (i.e. number of required sites) to estimate areal mean soil moisture with correlation coefficient ⩾0.99 and root mean square difference ⩽0.02m3 m−3. This provides guidance for future soil moisture network establishment in similar regions; (b) both number of required sites and the most representative site are insensitive to temporal scales while conversely sensitive to soil wetness conditions; (c) the combination of a few optimally-selected sites can give more robust estimate of areal mean soil moisture than a single site does because the former contains more information on spatial heterogeneity. These findings can provide not only a practical compromise between maintenance cost and risk on reliability for an existing soil moisture network, but also insights for soil moisture upscaling studies and satellite soil moisture products validations. [ABSTRACT FROM AUTHOR]

Summary: Alpine meadow soil is an important ecosystem component of the Qinghai-Tibetan Plateau. However, the alpine meadow soil is undergoing serious degradation mainly due to global climate change, overgrazing, human activities and rodents. In this paper, spatial sequencing was chosen over time succession sequencing to study the changes of soil hydraulic properties under different degrees of alpine meadow degradation. Soil saturated hydraulic conductivity (Ks) and Gardner α both at the surface and at 40–50cm depth were investigated in the field using tension infiltrometers. Soil physical and chemical properties, together with the root index at 0–10cm and 40–50cm soil layer depths were also analyzed. Pearson correlations were adopted to study the relationships among the investigated factors and principal component analysis was performed to identify the dominant factor. Results show that with increasing degree of degradation, soil sand content increased while soil Ks and Gardner α as well as soil clay content, soil porosity decreased in the 0–10cm soil layers, and organic matter and root gravimetric density decreased in both the 0–10cm and 40–50cm soil layers. However, soil moisture showed no significant changes with increasing degradation. With decreasing pressure head, soil unsaturated hydraulic conductivity reduced more slowly under degraded conditions than non-degraded conditions. Soil Ks and Gardner α were significantly correlated (P =0.01) with bulk density, soil porosity, soil organic matter and root gravimetric density. Among these, soil porosity is the dominant factor explaining about 90% of the variability in total infiltration flow. Under non-degraded conditions, the infiltration flow principally depended on the presence of macropores. With increasing degree of degradation, soil macropores quickly changed to mesopores or micropores. The proportion of total infiltration flow through macropores and mesopores significantly decreased with the most substantial decrease observed for the macropores in the 0–10cm soil layer. The substantial decrease of macropores caused a cut in soil moisture and hydraulic conductivity. This study improves the understanding and prediction of alpine meadow soil and ecosystem changes and provides guidelines for improving water flow modeling under the background of global climate change over the Qinghai-Tibetan Plateau and similar regions. [Copyright &y& Elsevier]