Statistical assessment and hydrological utility of the latest multi-satellite precipitation analysis IMERG in Ganjiang River basin Na Li a,b , Guoqiang Tang c , Ping Zhao b , Yang Hong c,d, ⁎, Yabin Gou e , Kai Yang f a Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science & Technology, Nanjing 210044, China b State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Science, Beijing 100081, China c State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China d Department of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73072, United States e Hangzhou Meteorological Bureau, Hangzhou 3010051, China f Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China abstract article info Article history: Received 5 December 2015 Received in revised form 19 July 2016 Accepted 19 July 2016 Available online 21 July 2016 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 prod- uct 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. © 2016 Published by Elsevier B.V. Keywords: Precipitation Radar GPM IMERG Hydrometeorology CREST hydrologic model 1. Introduction Severe storms cause disastrous floods and landslides, posing a great threat to personal security and national security as well as to the economy (Pfeifroth et al., 2015). Hydrologic models provide critical information for studying the water cycle and are irreplaceable for streamflow simulation, water resources management, and disaster prediction (e.g., floods and landslides) around the world (Kidd and Huffman, 2011). However, the availability of high-quality rainfall forcing data is a prerequisite for conducting meaningful hydrologic studies (Ali et al., 2005). At present, surface precipitation is generally measured using three approaches: (1) rain gauges, (2) ground-based weather radars, and (3) satellite sensors. Rain gauges provide point-based surface rainfall data (Brommundt and Bárdossy, 2007; Dubois et al., 1998; Nystuen et al., 1996), which are usually interpolated to provide spatially Atmospheric Research 183 (2017) 212–223 ⁎ Corresponding author at: State Key Laboratory of Hydroscience and Engineering and Dept. of Hydraulic Engineering, Tsinghua University, Room A207, Beijing 100084, China. E-mail address: hongyang@tsinghua.edu.cn (Y. Hong). http://dx.doi.org/10.1016/j.atmosres.2016.07.020 0169-8095/© 2016 Published by Elsevier B.V. Contents lists available at ScienceDirect Atmospheric Research journal homepage: www.elsevier.com/locate/atmosres