Xijiang river basin is located in southern China and characterized by the subtropical climate. The industrialization has been increasing rapidly in recent decades, and the population of Xijiang river basin has exceeded 100 million. Therefore, the damages and losses caused by flood and drought disasters may increase (Yuan et al., 2017, Zhang et al., 2016). The realistic portrayal of spatiotemporal rainfall estimation is very important to improve flood and drought predicting technology and early warning strategy in Xijiang river basin. With the development of advancing algorithms, hydrological satellites play a more and more critical role in obtaining precipitation data.

Recent rainfall algorithms based on brightness temperatures from passive microwave and thermal infrared sensors are adopted to estimate the rainfall process. The National Oceanic and Atmospheric Administration’s Climate Prediction Center morphing technique is one of such algorithms (CMORPH_V1.0-DLY; Xie et al., 2017). PERSIANN-Cloud Classification System, which used the variable threshold cloud segmentation algorithm, has also been well applied (PERSIANN-CCS; Hong et al., 2004). The TRMM Multi-satellite Precipitation Analysis datasets, available in near and post real-time or delayed modes, are the most widely used high-resolution quasi-global multi-satellite rainfall datasets (TRMM_3B42_DAILY_HQprecipitation, TRMM_3B42_DAILY_IRprecipitation; Huffman et al., 2015; Mitra et al., 2013). The mission-continued GPM has better spaceborne precipitation radar instruments which could provide high spatiotemporal resolution and sizeable spatial coverage datasets (GPM_3IMERGDF_PrecipitationCal; Tan and Santo, 2018). Meanwhile, JAXA has developed the Global Satellite Mapping of Precipitation based on the GPM mission (GSMaP_GAUGE_NRT; Zhou et al., 2020). The comparison between the SPEs has been conducted in previous studies, and the above-mentioned products are commonly used with considerable accuracy in regional evaluations (Tang et al., 2020, Alsumaiti et al., 2020, Chen et al., 2013, Hong et al., 2007, Lu and Yong, 2020). Therefore, this study intends to evaluate and select the most suitable one for Xijiang river basin among them.

Previous studies tend to ignore the influence of different seasons on SPEs (Li et al., 2020, Wang et al., 2017). The capacity of SPEs varied in detecting heavy, moderate, and light rainfall events. The areas such as Xijiang river basin influenced by tropical and subtropical monsoons are distinguished by monsoon and dry seasons, and therefore the SPEs should be evaluated in the monsoon and dry seasons, respectively. Additionally, although comparison with rain-gauge observations that are considered as the “true value” with high reliability could estimate the accuracy of the SPEs to a certain extent, evaluating SPEs based on their predictive ability in a hydrological modelling framework would provide important information on hydrological applications (Chen et al., 2018). Errors in Satellite-derived Precipitation Estimate datasets could propagate into hydrological simulations. Evaluating SPEs by the performance of SPE-driven streamflow simulations has been proven successful (Duan et al., 2019, Le et al., 2020, Lakew et al., 2020). However, evaluation based on streamflow simulation focuses on analyzing the overall accuracy of SPEs, in terms of time series. The surface soil moisture simulation, however, could identify the hydrological accuracy of SPEs on a spatial scale. The upstream surface soil moisture has a specific correlation with the downstream streamflow, which can be used to study the spatial consistency of the accuracy of SPEs. To date, insufficient attention has been paid to the evaluation of SPEs by the performance of SPE-driven soil moisture simulations (Bhuiyan et al., 2020).

In this study, the six SPEs are evaluated in the dry and monsoon seasons over Xijiang river basin. Simulations of streamflow and surface soil moisture are conducted simultaneously using the CREST-Kinematic wave routing model to evaluate the hydrological accuracy of SPEs. The main objectives of the process are (1) to examine the quantitative accuracy of rainfall amount estimates of SPEs in dry and monsoon seasons; (2) to measure the detection capacity of SPEs at different rain regimes; (3) to evaluate the performances of SPEs on simulating streamflow and surface soil moisture. The experiments are conducted on a daily scale from 2008 to 2012. The results are expected to deepen the understanding of the error characteristics of the SPEs and find the most suitable SPEs for application in Xijiang river basin.