## 5 Conclusions  
The present work employed two bias correction methods (SMBC, RBC) with two bias factors (month, 10-day) to correct two NRT SPPs (GSMaP-NRT, IMERG-Early) and evaluated both the original and corrected SPPs with comprehensive evaluation indicators. Then, the best-corrected SPPs (GSMaP-RBC, IMERG-RBC) were selected as precipitation in driving the BTOP to simulate flood discharge. The main conclusions are as follows:  
(1) Bias correction is necessary and effective for NRT SPPs. All correction methods improved NRT SPPs effectively, of which RBC-10 day has the best performance. However, for the heterogeneity of precipitation in time and space, the statistical correction has certain limitations. Therefore, it is necessary to introduce the dynamic bias correction approach utilizing available ground rainfall data at higher temporal scales (e.g., daily) in future work.  
(2) The consistency of SPPs with gauge rainfall improves as the spatial and temporal scale increases. It is significant for studies on a watershed scale or large scale. This principle is also applicable to runoff simulation of watersheds.  
(3) The GSMaP-NRT and IMERG-Early show a considerable potential to simulate flood discharge in the Fuji River. Although precipitation and hydrological processes are heterogeneous in time and space, the BTOP runoff simulation results at hourly steps are satisfactory using the corrected GSMaP-NRT and IMERG-Early as forcing precipitation. At the same time, it suggests a high potential for the application of NRT SPPs in many data-sparse watersheds in the world.  
(4) The present work applied a robust hydrological model in the Fuji River basin. Moreover, a comprehensive evaluation system has been introduced. According to this paper, the corrected SPPs and hydrological simulation show good results with sufficient rainfall stations. Future work will focus on the impacts of the pattern of the gauge network.  
Figure 7: Scatterplots between observed and simulated discharge for five precipitation datasets and five cases. (**a**)–(**e**) represent five cases respectively.  
We will take the Fuji River basin as a case to study the gauge network's impact on bias correction of SPPs and hydrological simulation. To further figure out how to improve the hydrological efficiency in the case of a limited gauge network.  
In general, the results showed that the NRT SPPs could be valuable alternative data for flood forecasting in other river basins and can provide references for applying NRT SPPs in other basins, particularly in poor- or un-gauged river basins for real-time flood simulation, flood early warning, even flood extent mapping for disaster assessment and management.  
Conceptualization, L.Z., K.T. and T.K.; methodology, L.Z. and M.R.; software, L.Z. and H.S.; formal analysis, L.Z., M.R., K.T., T.K. and H.S.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z., M.R., K.T., T.K. and T.A.; visualization, L.Z. and H.S.; supervision, K.T., T.K. and T.A.; funding acquisition, T.A. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Regional Innovation Cooperation Program from the Science& Technology Department of Sichuan Province, grant number 2020 YEQ0013 and China Scholarship Council, grant number 201806240035.  
Not applicable.  
The study did not report any data.  
The authors thank the anonymous referees for their valuable comments and suggestions.  
The authors declare no conflict of interest.