Long‐term hydrological assessment of remote sensing precipitation from multiple sources over the lower Yangtze River basin, China.

Multi‐source remote sensing precipitation products are being used more and more frequently for rainfall analysis and hydrological applications around the world. The aim of the present study was to assess the variances and uncertainty of long‐term precipitation and corresponding hydrological simulations from 1984 to 2016 using rain gauge measurements, two satellite rainfall products retrieved from the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Record (PERSIANN‐CDR) respectively, along with rainfall reanalysis data from the ERA‐Interim over the lower Yangtze River basin, China. The results indicated overestimation from ERA‐Interim and PERSIANN‐CDR on seasonal rainfall. CPC rainfall estimation had marginal bias with rain gauge measurements among all the remote sensing rainfall products on a daily scale, while the ERA‐Interim tended to overestimate when the rainfall rate was less than 50 mm. On a monthly scale, CPC rainfall estimations were very close to the rain gauge measurements, with a correlation coefficient of 0.96, whilst the ERA‐Interim rainfall products showed significant overestimation in February, March and September. Nevertheless, all remote sensing rainfall products achieved good performance in June and July, showing promising potential to be an alternative to rainfall data for long‐term water resource analysis. Furthermore, ERA‐Interim rainfall products did not perform as well as the other rainfall sources in flow simulations, as evidenced by the large root‐mean‐square error and bias. CPC rainfall products outperformed all other rainfall sources in terms of the Nash–Sutcliffe coefficient at all temporal scales. In addition, the skill scores suggested that the performance of the streamflow simulation improved significantly on a monthly scale, with a higher Nash–Sutcliffe coefficient and smaller total streamflow errors. [ABSTRACT FROM AUTHOR]