Satellite retrieval and land surface models have become the mainstream methods for monitoring soil moisture (SM) over large regions; however, the uncertainty and coarse spatial resolution of these products limit their applications at the regional and local scales. We proposed a hybrid approach combining the triple collocation (TC) and the long short-term memory (LSTM) network, which was designed to generate a high-quality SM dataset from satellite and modeled data. We applied the proposed approach to merge SM data from Soil Moisture Active Passive (SMAP), Global Land Data Assimilation System-Noah (GLDAS-Noah), and the land component of the fifth generation of European Reanalysis (ERA5-Land), and we then downscaled the merged SM data from 0.36° to 0.01° resolution based on the relationship between the SM data and auxiliary environmental variables (elevation, land surface temperature, vegetation index, surface albedo, and soil texture). The merged and downscaled SM results were validated against in situ observations. The results showed that: (1) the TC-based validation results were consistent with the in situ-based validation, indicating that the TC method was reasonable for the comparison and evaluation of satellite and modeled SM data. (2) TC-based merging was superior to simple arithmetic average merging when the parent products had large differences. (3) Downscaled SM of the TC-based merged product had better performance than that of the parent products in terms of ubRMSE and bias values, implying that the fusion of satellite and model-based SM data would result in better downscaling accuracy. (4) Downscaled SM of TC-based merged data not only improved the representation of the SM spatial variability but also had satisfactory accuracy with a median of R (0.7244), ubRMSE (0.0459 m³/m³), and bias (−0.0126 m³/m³). The proposed approach was effective for generating a SM dataset with fine resolution and reliable accuracy for wide hydrometeorological applications.

Understanding the temporal inequality in precipitation is of great importance for water resource management, environmental risk management, and ecological conservation. This study investigated the spatial patterns and trends of the daily precipitation concentration over the Yarlung Tsangpo River Basin using the concentration index (CI) and the Lorentz asymmetric coefficient (LAC). A Mann–Kendall test and Hurst’s rescaled range analysis were used to detect the change in CI trends. The CI ranged from 0.58 to 0.65, suggesting that a quarter of the rainiest days contributed approximately 69–78% of the total precipitation. The LAC analysis indicated that the nonuniform distribution of precipitation was mainly attributed to a large proportion of days with light rainfall. Compared with that of the central region, the daily precipitation in the western and eastern regions was more irregular. At a seasonal scale, the dry season had a less homogeneous spatial distribution of CI compared to that of the wet season. Most areas exhibited no significant trends in CI from 1970 to 2017. A quarter of the stations presented a significant downward trend in CI, which were primarily found in the central and northern regions. In addition, the future trends of CI in most areas mostly agree with those of the current state; however, the majority of stations exhibited an uneven precipitation distribution in the dry season.