Abstract
Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) final run and the TRMM Multi-satellite Precipitation Analysis 3B42V7 precipitation products, and their feasibility in streamflow simulations in the Chindwin River basin, Myanmar, from April 2014 to December 2015 was also assessed. Results show that, although IMERG and 3B42V7 can potentially capture the spatiotemporal patterns of historical precipitation, the two products contain considerable errors. Compared with 3B42V7, no significant improvements were found in IMERG. Moreover, 3B42V7 outperformed IMERG at daily and monthly scales and in heavy rain detections at four out of five gauges. The large errors in IMERG and 3B42V7 distinctly propagated to streamflow simulations via the Xinanjiang hydrological model, with a significant underestimation of total runoff and high flows. The bias correction of the satellite precipitation effectively improved the streamflow simulations. The 3B42V7-based streamflow simulations performed better than the gauge-based simulations. In general, IMERG and 3B42V7 are feasible for use in streamflow simulations in the study area, although 3B42V7 is better suited than IMERG.
Highlights
Precipitation is an important component of the hydrological cycle
The latest Global Precipitation Measurement (GPM) Core Observatory sensors and new calibration algorithms were developed to improve the performance of GPM constellation satellites in observing precipitation at much finer spatiotemporal scales than their predecessor, Tropical Rainfall Measuring Mission (TRMM)
A few recent studies have proven that the GPM Integrated Multi-satellite Retrievals for GPM (IMERG) products are generally superior to TRMM in several regions, such as the Xinjiang region [30] and the Qinghai-Tibetan Plateau in China [30,31]; Mainland China [33,34]; Guilan, Bushehr, Kermanshah, and Tehran regions in Iran [37]; Far-East Asia [35]; and India [36]
Summary
Precipitation is an important component of the hydrological cycle. Accurate observation or estimation of precipitation is critical to flood forecasting and warning, drought monitoring, and water resources management [1]. The accuracy of the precipitation input significantly influences the performance of hydrological models. Obtaining accurate precipitation data for hydrological simulations and predictions is a challenging task in regions with complex terrains that have no or sparse rain gauge networks. The rapid development of satellite remote sensing technology has provided hydrologists an unprecedented opportunity to better estimate precipitation for hydrological applications. A few satellite precipitation products, such as the Precipitation Estimation from Remotely
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