Rainfall information is a prerequisite to and plays a vital role in driving hydrological models. However, limited by the observation methods, the obtained precipitation data, at present, are still too coarse. In this study, a new downscaling method was proposed to obtain high spatial resolution (~1 km/hourly) precipitation estimates based on Integrated Multi-satellitE Retrievals for GPM (IMERG) data at hourly scale. Compared with original IMERG data, the downscaled precipitation results showed the similar spatial patterns with those of original IMERG data, but with finer spatial resolution. In addition, the downscaled precipitation estimates were further analyzed to quantify their improvements using the Coupled Routing and Excess STorage (CREST) model across Ganjiang River basin. Compared with the observed streamflow, the downscaled precipitation results showed satisfying hydrological performance, with Nash-Sutcliffe Coefficient of Efficiency (NSCE), Root Mean Square Error (RMSE), Relative Bias (BIAS), and Correlation Coefficient (CC). The improvement in terms of four statistic metrics in terms of streamflow simulation also indicated great potential of hydrological utility for the downscaled precipitation results.

High-quality precipitation data are vital for hydrological applications and climate change research. In this study, we evaluated two satellite-based precipitation products from Chinese Fengyun (FY) 2G (G is one of the third batch of operational geostationary satellites in the Chinese FY-2 series) and the Global Precipitation Measurement (GPM) against the rain gauge-based precipitation data, respectively, over the Yunnan-Kweichow Plateau, China, at different temporal scales (e.g., monthly, daily, and hourly). And the main findings of this study were as follows: (1) FY2G Quantitative Precipitation Estimation (QPE) and Integrated Multisatellite Retrievals for GPM (IMERG) shared similar spatial precipitation patterns with those of rain gauge data, while the FY2G QPE general underestimated the precipitation (bias, −30%-0%), and IMERG obviously overestimated the precipitation (bias, 0%-60%) at monthly scale over the Yunnan-Kweichow Plateau; (2) the FY2G QPE correlated significantly better (Correlation coefficient, CC, 0.80) than IMERG (CC~0.20) against rain gauge data at daily scale, meanwhile, the average daily bias value of IMERG (~30%) was around 6 times larger than that of FY2G QPE (~5%); (3) The FY2G QPE (CC 0.7, bias~− 8%, root-mean-square error~2.0 mm/hr, mean absolute error~0.6 mm/hr) also generally outperformed the IMERG (CC~0.1, bias~13%, root-mean-square error~3.0 mm/hr, mean absolute error1.0 mm/hr), at hourly scale; (4) The FY2G QPE significantly underestimated the precipitation in the period between 16:00 and 20:00 with the probability of detection (POD) decreased from 0.8 to 0.5, while the IMERG significantly overestimated the precipitation in the period between 4:00 and 12:00, at diurnal scale; and (5) one of the reasons resulting the significant overestimations of IMERG might due to its weak abilities in detecting the precipitation events with POD~0.2 and false alarm ratio (FAR)~0.7 at diurnal scale, which performed worse than those of the FY2G QPE (POD~0.8 and FAR~0.4). These findings would provide valuable recommendations for using these satellite-based precipitation products in various application fields, such as hydrology, agriculture, and meteorology over the YKP, and also for improving the algorithms of GPM. . (2020). Spatiotemporal assessments on the satellite-based precipitation products from Fengyun and GPM over the Yunnan-Kweichow Plateau, China. Earth and Space Science, 7, e2019EA000857. https://doi.

Accurate estimation of evapotranspiration (ET) and its components is critical to developing a better understanding of climate, hydrology, and vegetation coverage conditions for areas of interest. A hybrid dual-source (H-D) model incorporating the strengths of the two-layer and two-patch schemes was proposed to estimate actual ET processes by considering varying vegetation coverage patterns and soil moisture conditions. The proposed model was tested in four different ecosystems, including deciduous broadleaf forest, woody savannas, grassland, and cropland. Performance of the H-D model was compared with that of the Penman-Monteith (P-M) model, the Shuttleworth-Wallace (S-W) model, as well as the Two-Patch (T-P) model, with ET and/or its components (i.e., transpiration and evaporation) being evaluated against eddy covariance measurements. Overall, ET estimates from the developed H-D model agreed reasonably well with the ground-based measurements at all sites, with mean absolute errors ranging from 16.

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Accurate satellite-based quantitative precipitation estimates (QPE) with high-quality and fine spatio-temporal resolutions play crucial roles in the meteorological analysis and the research of the global water cycle. The FY-4A is the first satellite of China's FengYun4 satellite series (FY4 series), the most recent and advanced generation of meteorological satellite operated by China. In this study, China Merged Precipitation Analysis (CMPA, 0.1°/hourly) dataset, generated by combining data of Automatic Weather Stations (AWS) with the Climate precipitation center Morphing (CMORPH), was adopted to calibrating and evaluating the FY-4A QPE (0.04/ °/half-hourly) by Spatio-Temporal Disaggregation Calibration Algorithm (STDCA), over Yunnan-Kweichow Plateau (YKP). Additionally, we generate new precipitation data called CFY QPE by combining FY4A QPE with CMPA based on STDCA, which has the finer spatiotemporal resolution and higher-quality data. The results indicate that CMPA is suitable in anchoring FY4A QPE, and the systematic biases and random errors of CFY QPE are significantly reduced, especially with a better correlation against gauge in terms of CC (~0.78) and mKGE (~0.7). Besides, the capabilities of capturing precipitation events on CFY QPE significantly improved with POD (~0.82) and FAR (~0.25). Collectively, the significant advantages of STDCA are shown in improving the quality of FY4A QPE.