Evaluation of the latest satellite-gauge precipitation products and their hydrologic applications over the Huaihe River basin

Satellite-gauge quantitative precipitation estimate (QPE) products may reduce the errors in near real-time satellite precipitation estimates by combining rain gauge data, which provides great potential to hydrometeorological applications. This study aims to comprehensively evaluate four of the latest satellite-gauge QPEs, including NASA's Tropical Rainfall Measuring Mission (TRMM) 3B42V7 product, NOAA's Climate Prediction Center (CPC) MORPHing technique (CMORPH) bias-corrected product (CMORPH CRT), CMORPH satellite-gauge merged product (CMORPH BLD) and CMORPH satellite-gauge merged product developed at the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA) (CMORPH CMA). These four satellite-gauge QPEs are statistically evaluated over the Huaihe River basin during 2003-2012 and applied into the distributed Variable Infiltration Capacity (VIC) model to assess hydrologic utilities. Compared to the China Gauge-based Daily Precipitation Analysis (CGDPA) newly developed at CMA/ NMIC, the four satellite-gauge QPEs generally depict the spatial distribution well, with the underestimation in the southern mountains and overestimation in the northern plain of the Huaihe River basin. Specifically, both TRMM and CMORPH CRT adopt simple gauge adjustment algorithms and exhibit relatively poor performance, with evidently deteriorated quality in winter. In contrast, the probability density function-optimal interpolation (PDF-OI) gauge adjustment procedure has been applied in CMORPH BLD and CMORPH CMA, resulting in higher quality and more stable performance. CMORPH CMA further benefits from a merged dense gauge observation network and outperforms the other QPEs with significant improvements in rainfall amount and spatial/temporal distributions. Due to the insufficient gauge observations in the merging process, CMORPH BLD features the similar error characteristics of CMORPH CRT with a positive bias of light precipitation and a negative bias of heavy precipitation, in contrast to the overall large overestimation by TRMM. The quality of QPEs directly impacts streamflow simulations, as the precipitation biases are propagated into simulated streamflow through interaction with hydrologic processes. The general streamflow pattern is well captured at multiple time scales by the simulations using the four satellite-gauge QPEs as the input forcing. CMORPH CRT shows the worst simulations in both long-term streamflow and extreme flood events, while CMORPH CMA forced streamflow simulations even outperform that forced by CGDPA. CMORPH CMA is able to reproduce the July 2003 flood event, while the other three QPEs fail to generate such extreme flood. Overall, CMORPH CMA shows great potential to improve the precipitation distribution and hydrometeorological simulations, and can serve as an alternative high quality QPE in China. (C) 2016 Elsevier B.V. All rights reserved.