Global soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) at daily 1-km resolution from 2000 to 2020. This is achieved based on the European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product at 0.25° resolution. Firstly, an operational gap-filling method was developed to fill the missing data in the ESA-CCI SSM product using SSM of the ERA5 reanalysis dataset. Random Forest algorithm was then adopted to disaggregate the coarse-resolution SSM to 1-km, with the help of International Soil Moisture Network in-situ observations and other optical remote sensing datasets. The generated 1-km SSM product had good accuracy, with a high correlation coefficent (0.89) and a low unbiased Root Mean Square Error (0.045 m³/m³) by cross-validation. To the best of our knowledge, this is currently the only long-term global gap-free 1-km soil moisture dataset by far.

Model calibration and validation are challenging in poorly gauged basins. We developed and applied a new approach to calibrate hydrological models using distributed geospatial remote sensing data. The Soil and Water Assessment Tool (SWAT) model was calibrated using only twelve months of remote sensing data on actual evapotranspiration (ETa) geospatially distributed in the 37 sub-basins of the Lake Chad Basin in Africa. Global sensitivity analysis was conducted to identify influential model parameters by applying the Sequential Uncertainty Fitting Algorithm–version 2 (SUFI-2), included in the SWAT-Calibration and Uncertainty Program (SWAT-CUP). This procedure is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. The sensitivity analysis led us to identify fifteen influential parameters, which were selected for calibration. The optimized parameters gave the best model performance on the basis of the high Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and determination coefficient (R²). Four sets of remote sensing ETa data products were applied in model calibration, i.e., ETMonitor, GLEAM, SSEBop, and WaPOR. Overall, the new approach of using remote sensing ETa for a limited period of time was robust and gave a very good performance, with R² > 0.9, NSE > 0.8, and KGE > 0.75 applying to the SWAT ETa vs. the ETMonitor ETa and GLEAM ETa. The ETMonitor ETa was finally adopted for further model applications. The calibrated SWAT model was then validated during 2010–2015 against remote sensing data on total water storage change (TWSC) with acceptable performance, i.e., R² = 0.57 and NSE = 0.55, and remote sensing soil moisture data with R² and NSE greater than 0.85.

Information on global canopy rainfall interception loss is essential for understanding the dynamics of land surface processes and the water cycle. In addition, large uncertainty remains regarding the global variation in this factor. The development of satellite earth observations has provided a great opportunity for the estimation of global rainfall interception loss to reveal the spatiotemporal variations. In the current study, the analytical Gash model was adapted and applied to estimate global rainfall interception loss from 2001 to 2015 on the basis of satellite remote‐sensing products, for example, gross rainfall amount and rate and leaf area index. The Dalton‐type equation was adopted to estimate the wet canopy evaporation rate in the revised Gash model. The estimation on the basis of the Dalton‐type equation showed better agreement with the ground observations than the classical Penman–Monteith equation in terms of estimated rainfall interception loss and its ratio to gross rainfall. Large spatial variations in global rainfall interception loss were found, and high values appeared in the regions with dense vegetation cover and high gross rainfall amounts, for example, tropical rainforests, whereas high rainfall interception to gross rainfall ratios occurred in the regions with low rain rates and high vegetation cover. This study constitutes a significant step forward in understanding global hydrological cycle on the basis of earth observation products.

Precipitation regimes have been predicted to shift to more extreme patterns that are characterized by heavy rainfall events and longer dry intervals. Spatiotemporal variation of precipitation extremes may significantly influence the local ecosystem, especially in the arid and semi‐arid regions. This study investigated the variations in extreme precipitation indices across the arid and semi‐arid land of China from 1960 to 2018. Spatial autocorrelation analysis was also conducted to evaluate spatial autocorrelations within the study area. There was generally an increasing trend of precipitation intensity, however, most stations in the low‐altitude semi‐arid regions showed non‐significant trend. The frequency indices generally showed a decreasing trend in consecutive dry days, and an increasing trend in precipitation days in the arid land and high‐altitude semi‐arid regions, suggesting that the arid and high‐altitude semi‐arid regions are becoming wetter. Signals of decreasing precipitation intensity and frequency were still found, such as increasing trends in consecutive dry days in the stations from the low‐altitude semi‐arid regions, implying the possibility of increased drought risk at small spatial scale. The global Moran's I values indicated that the spatial autocorrelation of the trend in the low‐altitude semi‐arid region is high. The local Moran's I showed that the number of stations with wetting trend in the arid region were much higher than that in the semi‐arid regions. These observations imply that the precipitation extremes in the arid and semi‐arid regions of China are becoming more homogeneous. Hence, this study can serve as a useful reference to the policymakers and stakeholders for framing water resource and ecosystem management strategies in arid regions.