Daily precipitation dataset at 0.1° for the Yarlung Zangbo River basin from 2001 to 2015

In order to obtain higher precision regional precipitation dataset in the Yarlung Zangbo River basin, two different schemes were proposed on the basis of the two most application potential satellite-based precipitation products, IMERG and CMORPH_BLD. The first method aimed to correct the positive error of IMERG based on high correlation (CC>0.9) between IMERG and gauges. The second algorithm was developed to merge IMERG with CMORPH_BLD by the stepwise linear regression. As the reference, IMERG played a key role in correction of precipitation ratio determination and precipitation event detection. Two daily datasets with 0.1° resolution (BRD_IMERG and IGREA_IMERG-CMORPH) performed better than IMERG in CC, RMSE, ME, FAR and CSI, and streamflow simulation in the whole basin (NS: 0.86 and 0.87; RBIAS: -19% and -11%) and sub-basins. The two proposed methods were relatively simple and efficient for reconstructing higher precision regional precipitation, and the datasets provided a good application demonstration in the alpine region.

为获取雅鲁藏布江流域更高精度的区域降水数据集，研究团队基于两款最具应用前景的星载降水产品IMERG与CMORPH_BLD，提出了两种不同的校正融合方案。第一种方法依托IMERG与地面雨量站间高度显著的相关性（相关系数CC>0.9，即Correlation Coefficient），旨在校正IMERG产品的正偏差。第二种算法通过逐步线性回归方法，实现IMERG与CMORPH_BLD的数据融合。其中IMERG被用作参考基准，在降水比例确定与降水事件检测环节中发挥了核心作用。两款分辨率为0.1°的逐日降水数据集（BRD_IMERG与IGREA_IMERG-CMORPH）在相关系数、均方根误差（Root Mean Square Error，RMSE）、平均误差（Mean Error，ME）、虚警率（False Alarm Rate，FAR）与临界成功指数（Critical Success Index，CSI）这几项指标上的表现均优于原始IMERG产品；在全流域（纳什效率系数（Nash-Sutcliffe Efficiency，NS）分别为0.86与0.87；相对偏差（Relative Bias，RBIAS）分别为-19%与-11%）及各子流域的径流模拟中同样表现出色。所提出的两种方法均具备简洁高效的特点，可用于重建高精度区域降水数据，且所生成的数据集在高寒区域展现出了优异的应用示范价值。