Abstract
Accurate rainfall estimates are important in many hydrologic activities. Rainfall data are retrieved from rain gauges (RGs), satellites, radars, and re-analysis products. The accuracy of gauge-based gridded precipitation products (GbGPPs) relies on the distribution of RGs and the quality of rainfall data records obtained from these. The accuracy of satellite-based precipitation products (SbPPs) depends on many factors, including basin climatology, basin topography, precipitation mechanism, etc. The hydrologic utility of different precipitation products was examined in many developed regions; however, less focused on the developing world. The Huai Bang Sai (HBS) watershed in north-eastern Thailand is a less focused but an important catchment that significantly contributes to the water resources in Thailand. Therefore, this research presents the investigation results of the hydrologic utility of SbPPs and GbGPPs in the HBS watershed. The efficiency of nine SbPPs (including 3B42, 3B42-RT, PERSIANN, PERSIANN-CCS, PERSIANN-CDR, CHIRPS, CMORPH, IMERG, and MSWEP) and three GbGPPs (including APHRODITE_V1801, APHRODITE_V1901, and GPCC) was examined by simulating streamflow of the HBS watershed through the Soil & Water Assessment Tool (SWAT), hydrologic model. Subsequently, the streamflow simulation capacity of the hydrological model for different precipitation products was compared against observed streamflow records by using the same set of calibrated parameters used for an RG simulated scenario. The 3B42 product outperformed other SbPPS with a higher Nash–Sutcliffe Efficiency (NSEmonthly>0.55), while APHRODITE_V1901 (NSEmonthly>0.53) performed fairly well in the GbGPPs category with closer agreements with observed streamflow. In addition, the CMORPH precipitation product has not performed well in capturing observed rainfall and subsequently in simulating streamflow (NSEmonthly<0) of the HBS. Furthermore, MSWEP and CHIRPS products have performed fairly well during calibration; however, they showcased a lowered performance for validation. Therefore, the results suggest that accurate precipitation data is the major governing factor in streamflow modeling performances. The research outcomes would capture the interest of all stakeholders, including farmers, meteorologists, agriculturists, river basin managers, and hydrologists for potential applications in the tropical humid regions of the world. Moreover, 3B42 and APHRODITE_V1901 precipitation products show promising prospects for the tropical humid regions of the world for hydrologic modeling and climatological studies.
Highlights
Precipitation is one of the major driving components of the hydrologic cycle [1].accurate precipitation estimates are essential for many professionals including meteorologists, hydrologists, ecologists, agriculturists, disaster management personals, energy planners, etc. for decision making and planning in various hydrologic related activities
There are some mismatches between observed and simulated streamflow with mixed results. These differences can clearly be seen for flood peaks during the rainy seasons. It is noteworthy, the flood peak in 2010 simulated by the Soil & Water Assessment Tool (SWAT) model from rain gauges (RGs) was comparable with observed discharge
The hydrologic performance of different precipitation products was examined by statistical indices, including the Nash–Sutcliffe Efficiency (NSE) and the R2, which were recommended by Moriasai et al [59]
Summary
Precipitation is one of the major driving components of the hydrologic cycle [1].accurate precipitation estimates are essential for many professionals including meteorologists, hydrologists, ecologists, agriculturists, disaster management personals, energy planners, etc. for decision making and planning in various hydrologic related activities. For decision making and planning in various hydrologic related activities. Precipitation estimates are obtained from rain gauges (RGs), meteorological radars, satellites, and re-analysis products. Technological and economic constraints limit the usage of high-tech weather radars in developing countries such as Thailand for hydrologic related applications [2]. Re-analysis data are produced through the combination of past short-range weather forecasts with observations through data assimilation. These re-analysis products can suffer from significant biases in the tropical regions [3,4,5]. Observed rainfall data are highly used in many hydrologic related applications. Interpolation of RG data is used to derive the gauge-based gridded precipitation products (GbGPPs)
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