Abstract
Rainfall information is a prerequisite to and plays a vital role in driving hydrological models. However, limited by the observation methods, the obtained precipitation data, at present, are still too coarse. In this study, a new downscaling method was proposed to obtain high spatial resolution (~1 km/hourly) precipitation estimates based on Integrated Multi-satellitE Retrievals for GPM (IMERG) data at hourly scale. Compared with original IMERG data, the downscaled precipitation results showed the similar spatial patterns with those of original IMERG data, but with finer spatial resolution. In addition, the downscaled precipitation estimates were further analyzed to quantify their improvements using the Coupled Routing and Excess STorage (CREST) model across Ganjiang River basin. Compared with the observed streamflow, the downscaled precipitation results showed satisfying hydrological performance, with Nash-Sutcliffe Coefficient of Efficiency (NSCE), Root Mean Square Error (RMSE), Relative Bias (BIAS), and Correlation Coefficient (CC). The improvement in terms of four statistic metrics in terms of streamflow simulation also indicated great potential of hydrological utility for the downscaled precipitation results.
Highlights
The frequency and intensity of extreme rainfall events and severe storms present an increasing trend during recent years, resulting in disastrous floods and landslides, which pose great threats to personal security and the economy [1,2]
The spatial patterns of mean hourly precipitation estimates measured by Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) and digital elevation model (DEM), with the spatial resolution of
There was an increasing trend of mean hourly precipitation estimates captured by IMERG, while the DEM demonstrated a decreasing trend
Summary
The frequency and intensity of extreme rainfall events and severe storms present an increasing trend during recent years, resulting in disastrous floods and landslides, which pose great threats to personal security and the economy [1,2]. Rain gauges provide point-based surface rainfall data, which have been always interpolated into gridded precipitation estimates for hydrological and related studies [8,12] In this case, the density of the rain gauges significantly affects the quality of the interpolated results, especially over the remote regions with limited rain gauge networks and complex topography [12,13,14,15,16]. Compared with traditional rain gauges, ground-based radars can obtain rainfall observations with near continuous representations of spatial and temporal variability, as well as with well captures of the 3-D distribution of the rainfall, over a relatively larger coverage [4,17], which makes it possible to provide radar quantitative precipitation estimates for hydrological applications at basin scale [8,18,19]. During the last three decades, various satellite-based precipitation estimates have been generated and delivered after a series of launched projects, e.g., the Tropical
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