Precipitation is one of the most essential components of the hydrological cycle [
1]. The quantity and quality of the precipitation data used as the principal input to hydrological models affect the accuracy of the simulation results [
2,
3]. Rain gauges provide direct precipitation measures; however, scarcity and irregularity problems with respect to the gauge network considerably influence the data reliability [
4,
5]. In addition, an effective spatial coverage of precipitation over a large area is difficult. Compared with rain gauges that provide rainfall data by accumulating rainfall over a time interval, weather radar systems provide an instantaneous spatial measure of precipitation and thus, produce rapid climate information [
6]. However, Westrick et al. [
7] investigated the limitations of the radar network for quantitative precipitation measurement and showed that the radar-derived precipitation estimates could not represent the regional precipitation since radar coverage is limited to lowland areas. The drawbacks of radar-derived data, such as coverage area limitations, costly infrastructure construction, and inaccuracy under complex atmospheric conditions, result in the poor performance of hydrological models [
8]. Currently, visible and thermal infrared sensors onboard the geostationary Earth-orbiting satellites and passive microwave sensors onboard the low-Earth-orbiting satellites provide more accurate rainfall estimates at a higher measurement frequency. Based on the advancements of these techniques, several satellite-based precipitation products with global high-resolution (up to 0.25°) are now available such as those derived from the Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center morphing technique (CMORPH) [
9]. Those global and near-real-time rainfall estimates are extremely attractive for hydrological and weather studies [
10,
11,
12]. The rainfall estimates from PERSIANN, CMORPH, and TRMM-based Multi-satellite Precipitation Analysis (TMPA), which combines satellite data from different sensors and ground station data from the Global Precipitation Climatology Centre, have been widely applied in numerous studies [
13,
14,
15,
16,
17,
18,
19,
20,
21]. Recently, the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS), which consist of reanalyzed data based on assimilation techniques, provided important basic data (that is, rainfall, maximum and minimum temperature, solar radiation, relative humidity, and wind speed) that are extremely useful to analyze climate–water cycles and “macro” energy balances in hydrological studies [
22,
23]. The CMADS was developed by Dr. Xianyong Meng from the China Institute of Water Resources and Hydropower Research (IWHR) and has received worldwide attention [
22]. Based on the full coverage of East Asia and an improved accuracy, CMADS promises to be one of the most useful satellite-derived weather datasets for meteorological and hydrological research.
Distributed hydrological models have been widely applied in water resource management and hydrological research [
24]. They include the Hydrologic Simulation Program-Fortran (HSPF) [
25], MIKE SHE [
26], the Hydrologic Modeling System (HEC-HMS) [
27], and the Soil and Water Assessment Tool (SWAT) [
28]. Those models reduce the dependency on specific precipitation inputs and fully use satellite-based hydrometeorological data [
29]. The SWAT has been widely applied because many studies showed that the SWAT model can simulate streamflow in regions with limited data well [
30,
31,
32]. Studies of SWAT applications in South Korea include those by Kang et al. [
33], Kim et al. [
34], Bae et al. [
35], Kim et al. [
36], Shope et al. [
37], and Cho et al. [
38]. Kim et al. [
34] suggested the integrated SWAT-MODFLOW model which can simulate the interaction between the river flow and the saturated aquifer. Kim et al. [
36] proposed a method for the evaluation of the flow regulation effects by dams on river flow using the SWAT model for the Han River Basin. In those studies, the SWAT model was successfully applied to mountainous areas and river basins with various sizes; however, a SWAT model using satellite-based rainfall data has not been considered.
Satellite-derived rainfall estimates with high spatial resolution contribute to the water resources management especially in areas where the ground-based climate data are limited. The hydrologic performance of different satellite rainfall products varies regionally because of several factors such as instrument characteristics and retrieval algorithms. Kim et al. [
39] compared four satellite precipitation products for the hydrological utility at a mountainous basin in South Korea and found that TMPAv6 and TMPAv7 products were closer to ground-based rainfall than CMORPH and global satellite mapping of precipitation (GSMaP). In the streamflow simulation, TMPAv6 and TMPAv7 performed well while CMORPH and GSMaP resulted in a large underestimation.
Although there are several flood control dams in the river, large floods have occurred in the downstream area of the Han River Basin, causing severe damages. The use of advanced techniques to predict precipitation that causes floods has attracted a large interest in recent years [
40]. The application of satellite precipitation data with high accuracy and resolution has been widely studied to be able to respond quickly in real-world situations. Numerous studies successfully applied the SWAT model or used satellite rainfall products for various regions of South Korea. However, combining satellite rainfall products with the SWAT model was not considered. This study investigates the hydrologic application of different satellite rainfall products in the Han River Basin of South Korea by applying the SWAT model. The CMADS, a newly developed dataset with the full coverage of East Asia, was evaluated in comparison with other satellite rainfall products. This is a case study for a specific period from 2008 to 2013 as the CMADS is only available from 2008. This study aimed to (1) compare four satellite rainfall products (TRMM 3B42 V7, PERSIANN, PERSIANN-CDR, and CMADS) with gauge rainfall data; and (2) evaluate the accuracy and suitability of the four satellite precipitation products as inputs for streamflow simulations in the Han River Basin. The results of the study can provide information on the performance of different satellite rainfall products in hydrologic modeling for the Han River Basin. In addition, this study contributes to enriching the scientific database on hydrologic applications of different satellite precipitation datasets, especially for data scarce regions.