## 2 Study Area and Data
### Satellite Data  
IMERG is the level 3 multi-satellite precipitation algorithm of GPM, which combined all available constellation microwave precipitation estimates, infrared (IR) satellite estimates, and monthly precipitation gauge data, with the intention of creating a new generation of global precipitation products [23]. The IMERG system is run several times, first to produce an \"early\" multi-satellite product (~4 h after observation time) for giving a preliminary estimate, and successively providing \"late\" multi-satellite products (~12 h after observation time) as more data arrive. When the monthly gauge data is received, the \"final\" satellite-gauge product (~2.5 months after the observation month) is created as research [47]. For these three IMERG products, in addition to the difference in sensor data latency, the early and late estimates use climatological gauge data, while the final product is adjusted by monthly Global Precipitation Climatology Centre (GPCC) gauge data [48]. Thereare two precipitation data field variables embedded in each IMRG product: precipitationCal and precipitationUncal. PrecipitationCal means a multi-satellite precipitation estimate with gauge calibration, and precipitationUncal presents the original multi-satellite precipitation estimate. For a more detailed description of IMRG algorithm, readers can refer to [PERSON] et al. [26; 47; 40]. In this study, we used both calibrated precipitation estimate and uncalibrated precipitation estimates from Final Run IMRG version 5 (hereafter referred to as \"IMERG-C\" and \"IMERG-UC\", respectively).  
GSMaP is a satellite-based precipitation map algorithm that combines various available PMW and IR sensors aiming to develop high-precision precipitation products. The GSMaP products are produced in several steps. Firstly, the instantaneous precipitation rate is retrieved based on the PMW radiometers from different satellite platforms, including GMI, advanced microwave scanning radiometer 2 (AMSR2), TRMM Microwave Imager (TMI), special sensor microwave imager/sounder (SSMIS), advanced microwave sounding unit-A (AMSU-A), and microwave humidity sounder (MHS) [49]. Then, the gaps between PMW-based estimates are propagated using the cloud motion vectors computed from geo-IR images, and a Kalman filter approach is applied to refine the precipitation rate [50; 51]. Next, the forward and backward propagated precipitation estimates are weighted and combined to generate the GSMaP-MVK product. Unlike GSMaP-MVK, GSMaP-NRT is a simplified algorithm that only considers temporary forward cloud movement to keep operability and data latency in near real time. The GSMaP-Gauge is a gauge-calibrated product that adjusts the GSMaP-MVK estimate with National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center (CPC) gauge-based analysis of global daily precipitation [52]. In the current study, GSMaP-MVK and GSMaP-Gauge with the latest version 7 were used.  
Overall, four satellite-based precipitation datasets (IMERG-UC, IMERG-C, GSMaP-MVK, and GSMaP-Gauge), from April 2014 to March 2017, were selected for analysis in this study. Basic information of the four datasets is provided in Table 1. IMRG-UC and GSMaP-MVK belong to the \"satellite-only\" class, while IMRG-C and GSMaP-Gauge are \"gauge-adjusted\" products. In order to keep consistent with the ground reference dataset, both IMRG and GSMaP products were aggregated into daily readings, with 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) resolution. Generally, satellite precipitation products perform better when scaled up to a larger spatial average and longer time periods [48; 53]. Readers should keep in mind that the resampling might introduce additional uncertainties.  
Figure 1: Map and topography of the Tibetan Plateau (TP), meteorological stations, and Tangnaihai hydrological station.