Statistical assessment and hydrological utility of the latest multi-satellite precipitation analysis IMERG in Ganjiang River basin

• Statistical and hydrological evaluation of hourly IMERG over Ganjiang River basin
• IMERG performs well in the hydrological simulation based on Gauge- and RQPE-calibrated parameter sets
• Both IMERG and RQPE can estimate precipitation adequately, but RQPE outperforms IMERG
• The streamflow simulated by rain gauge-interpolated data has the highest NSCE value
• RQPE outperforms Gauge-based precipitation and hourly IMERG in all validation scenarios

This study aims to statistically and hydrologically assess the hydrological utility of the latest Integrated Multi-satellitE Retrievals from Global Precipitation Measurement (IMERG) multi-satellite constellation over the mid-latitude Ganjiang River basin in China. The investigations are conducted at hourly and 0.1° resolutions throughout the rainy season from March 12 to September 30, 2014. Two high-quality quantitative precipitation estimation (QPE) datasets, i.e., a gauge-corrected radar mosaic QPE product (RQPE) and a highly dense network of 1200 rain gauges, are used as the reference. For the implementation of the study, first, we compare IMERG product and RQPE with rain gauge-interpolated data, respectively. The results indicate that both remote sensing products can estimate precipitation fairly well over the basin, while RQPE significantly outperforms IMERG product in almost all the studied cases. The correlation coefficients of RQPE (CC = 0.98 and CC = 0.67) are much higher than those of IMERG product (CC = 0.80 and CC = 0.33) at basin and grid scales, respectively. Then, the hydrological assessment is conducted with the Coupled Routing and Excess Storage (CREST) model under multiple parameterization scenarios, in which the model is calibrated using the rain gauge-interpolated data, RQPE, and IMERG products respectively. During the calibration period (from March 12 to May 31), the simulated streamflow based on rain gauge-interpolated data shows the highest Nash–Sutcliffe coefficient efficiency (NSCE) value (0.92), closely followed by the RQPE (NSCE = 0.84), while IMERG product performs barely acceptable (NSCE = 0.56). During the validation period (from June 1 to September 30), the three rainfall datasets are used to force the CREST model based on all the three calibrated parameter sets (i.e., nine combinations in total). RQPE outperforms rain gauge-interpolated data and IMERG product in all validation scenarios, possibly due to its advantageous capability in capturing high space-time variability of precipitation systems in the humid climate during the validation period. Overall, RQPE and rain gauge-interpolated data exhibit better performance compared with the newly available IMERG product, and RQPE is better than rain gauge-interpolated data to some extent due to the combination of both radar and rain gauge observations. IMERG-forced hourly CREST hydrologic model based on the Gauge- and RQPE-calibrated parameters performs well over Ganjiang River basin. Future studies should promote the hydrological application of RQPE datasets at global and local scales, and continuously improve IMERG algorithms.