## 2 Materials and Methods
### Precipitation Estimates  
Precipitation estimates based on the GLDAS, MERRA, TRMM, GPM, and GPCP products as summarized in Table 2 were obtained from Giovanni Platform of the National Aeronautics and Space Administration (NASA) ([[https://giovanni.gsfc.nasa.gov/giovanni/](https://giovanni.gsfc.nasa.gov/giovanni/)]([https://giovanni.gsfc.nasa.gov/giovanni/](https://giovanni.gsfc.nasa.gov/giovanni/))). The GLDAS uses global land surface models to generate (develop) near real time products land surface fluxes and states by ingesting (being forced by ground- and satellite-based data [18]). The main objective of the GLDAS was to develop terrestrial surface model outputs that preserve the climatological consistency [18]. Since its development, GLDAS has developed a number of versions with improved products based on Noah, VIC, Mosaic, and Catchment land surface models. In this study, out of the three components of the GLDAS version 2 (GLDAS 2.0, GLDAS 2.1, and GLDAS 2.2), precipitation estimates from GLDAS 2.1 Noah Model 3.6 was used. GLDAS 2.1 does not include the data assimilation process, combines mode and surface observations, and has 3 h and monthly products since 2000. The 3 h estimates were aggregated to daily time scale to match that of the measurements. It should be noted that the GLDAS 2.1 was the forces with the GPCP V1.3 daily precipitation fields between 2000-2001 and AGREMET since 2001. The MERRA version 2 is a reanalysis of satellite observations projected conducted by the NASA Global Modeling and Assimilation Office (GMAO). MERRA uses the Goddard Earth Observing System Version 5 (GOES-5) model focusing on historic climate analysis, and its dataset provides precipitation estimates based on a large number of satellite observations and general circulation models [19; 20] in combination with weather stations observation to parameterize initial conditions. Version 5.12.4 of MERRA-2 uses microwave and hyperspectral infrared measurements, and it is available at 1 h temporal resolution. The 1 h estimates were aggregated to daily time scale to match that of the measurements. The TRMM Multi-Satellite Precipitation Analysis (TMPA) provided precipitation estimates by combining data from 3 sensors i.e., precipitation radar (PR), microwave imager (TMI), and visible and infrared (VIRS), as well as gauge observations [21]. TRMM era data is available from 1997 until 2019 as it seized operation in 2015. The science community transitioned to the GPM that was launched in 2014 with its improved precipitation product. The TRMM 3B42 v7 product was developed to maximize the quality of precipitation data for applied research in meteorology and hydrology globally [22]. It should be noted that more consistent TRMM 3B42 v7 data were the one available for the period between January 1998 to September 2014. After this period until December 2019, the 3B42 v7 algorithm was processed in parallel with GPM (IMERG). The TRMM data were available at the daily time scale--the same temporal resolution of that of the measurements. The GPM is considered a successor of TRMM with enhanced rain and snow observation. The GPM Core Observatory was launched in February 2014 with Dual-Frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) instruments [23] to create a global precipitation dataset [23]. The GPM has uses its Integrated Multi-Satellite Retrievals (IMERG) algorithm to develop a global precipitation dataset from a satellite constellation in collaboration with international members [23] (i.e., 6 passive microwave radiometers and 5 geosynchronous infrared sensors). The IMERG algorithm also fuses TRMM historic data (from 2000 to 2015) with the recent GPM (i.e., DPR and GMI) observations (from 2014 onward) to provide long-term precipitation dataset. The highest temporal resolution of