###### Abstract
As they are available at a shorter latency time (\(-4\) h), they have a great potential to be used for near real-time extreme weather monitoring, flood early warning, and flood impact assessments at both local and global scales [14; 15; 16]. Therefore, NRT SPPs are a useful alternative data source in the disaster warning and management of floods and droughts, especially in ungauged basins [17]. However, there is still a gap between observed precipitation and SPPs, especially the NRT SPPs [18; 19]. The main reason is that the accuracy of SPPs is affected by various factors, such as sampling uncertainty, indirect observation errors, and inversion algorithms [20]. Moreover, the characteristics of these factors will change with precipitation mechanisms, climatic zones, seasons, altitude, and terrain conditions. Although many studies focused on NRT SPPs [21; 22; 23], the applications of NRT SPPs in simulating flood discharge are relatively few due to their low accuracy since they are accompanied by large errors, especially in the range of local and catchment scale [24; 25]. The main reason is that the NRT products are prepared very quickly with shorter latency time by integrating limited available microwave observations from multiple satellites, and data unavailability regions are filled by interpolation or morphing techniques based on cloud movements from infrared (IR) data. Furthermore, other factors, such as indirect observation errors and inversion algorithms, also play an essential role in determining accuracy [20]. Therefore, it is essential to introduce bias correction methods to improve NRT SPPs before their practical application [26]. As a result, several previous studies focused on reducing biases or deviations in NRT SPPs with different methods such as mean factor correction method [27; 28], quantile mapping approach [29; 30], Bayesian method [31], multiplicative bias correction [32], and ratio bias correction method [32; 33]. Moreover, some new techniques like merging soil moisture with SPPs were introduced [34]. At the same time, the rainfall retrieval algorithms are also keeping updated to improve the rainfall estimation by introducing several changes for topographical correction [35], noise reduction [36], and precipitation type [37]. Therefore, scientific studies on the evaluation of NRT SPPs should be continued to assess their capability, identify biases, use bias correction methods to enhance their applicability in real-time monitoring and early warning application, and carry out disaster risk reduction and damage assessments in data poor or ungauged basins. Accordingly, this paper focused on enhancing the applicability of NRT SPPs (\(-4\) h latency) in driving the hydrological model for flood discharge simulation by identifying the proper method to correct NRT SPPs and evaluating bias correction and flood simulation performance. In order to fulfill the objectives above, this study investigated the performance of two statistical bias correction methods for NRT SPPs and their impacts on the accuracy of flood discharge simulation with the Block-wise use of the TOPMODEL (Topographic-based hydrologic model) (BTOP) model in a data-rich river basin, the Fuji River basin. GSMaP-NRT and IMERG-Early were selected as NRT SPPs. Two bias correction methods (simple multiplicative bias correction and ratio bias correction) with two bias factors (10-day and monthly) were employed to correct the NRT SPPs on different temporal scales. In addition, comprehensive evaluation criteria are used for evaluating both original and corrected NRT SPPs. Finally, the BTOP model was developed to simulate flood discharges at hourly steps with five types of precipitation inputs for each data source.