###### Abstract  
Flood management is an important topic worldwide. Precipitation is the most crucial factor in reducing flood-related risks and damages. However, its adequate quality and sufficient quantity are not met in many parts of the world. Currently, near real-time satellite precipitation products (NRT SPPs) have great potential to supplement the gauge rainfall. However, NRT SPPs have several biases that require corrections before application. As a result, this study investigated two statistical bias correction methods with different parameters for the NRT SPPs and evaluated the adequacy of its application in the Fuji River basin. We employed Global Satellite Mapping of Precipitation (GSMaP)-NRT and Integrated Multi-satellite Retrievals for GPM (IMERG)-Early for NRT SPPs as well as BTOP model (Block-wise use of the TOPMODEL (Topographic-based hydrologic model)) for flood runoff simulation. The results showed that the corrected SPPs by the 10-day ratio based bias correction method are consistent with the gauge data at the watershed scale. Compared with the original SPPs, the corrected SPPs improved the flood discharge simulation considerably. GSMaP-NRT and IMERG-Early have the potential for hourly river-flow simulation on a basin or large scale after bias correction. These findings can provide references for the applications of NRT SPPs in other basins for flood monitoring and early warning applications. It is necessary to investigate the impact of number of ground observation and their distribution patterns on bias correction and hydrological simulation efficiency, which is the future direction of this study. NRT SPPs; GSMaP; IMERG; bias correction; BTOP model; flood discharge simulation +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Adequacy of Near Real-Time Satellite Precipitation Products in Driving Flood Discharge Simulation in the Fuji River Basin, Japan. _Appl. Sci._**2021**, _11_, 1087. [[https://doi.org/10.3390/app11031087](https://doi.org/10.3390/app11031087)]([https://doi.org/10.3390/app11031087](https://doi.org/10.3390/app11031087))  
Received: 22 December 2020  
Accepted: 21 January 2021  
Submitted: 25 January 2021  
For a long time, hydrologists have mainly relied on a network of ground observation stations to obtain precipitation information. If only ground observations are used, one observation often represents actual rainfall in the range of hundreds, thousands, or even tens of thousands of square kilometers due to poor ground observation network. However, the actual precipitation is a weather process with significant variability in different time and space scales [4]. Insufficient spatial representation of the ground networks often leads to errors in estimating the spatial and temporal distribution of precipitation, which has become a primary source of uncertainty in hydrological simulations [5]. The satellite-based quantitative precipitation estimation technology has made significant progress in the past decades. The observation resolution and estimation accuracy have been continuously improving and have gradually become an essential tool for real-time rainfall monitoring for disaster early warning on global, regional, and catchment scales. It provides beneficial information on rainfall for hydrological studies in data-sparse basins [6; 7]. Many organizations and institutions around the world have released satellite precipitation products (SPPs) with various spatial and temporal resolution such as Tropical Rainfall Measuring Mission (TRMM) [8], Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIAN) [9], Climate Prediction Center Morphing Technique (CMORPH) [10], Global Satellite Mapping of Precipitation (GSMaP) [11; 12], and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) [13]. Now the near-real-time (NRT) satellite precipitation products (SPPs) have a high spatial and temporal resolution.