资源环境科技发展态势分析平台

Precipitation is an important climate variable to investigate extreme events (e.g. drought and flood) as well as to develop robust strategies for water resources planning and management. Lack of adequate and robust information on precipitation poses great difficulties in understanding the observed climate as well as to validate climate model outputs. To overcome this limitation gridded precipitation data sets have been constructed to supplement the lack of in situ data. This study compares five popular gridded precipitation data sets to evaluate their performance in terms of drought and wetness over Vietnam. These five gridded data sets include: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Climate Precipitation Center (CPC), (3) Climate Research Unit (CRU), (4) Global Precipitation Climatology Center (GPCC) and (5) University of Delaware (UDEL). The recently developed gridded precipitation observational data VnGP' from Vietnam is used as the reference data set to assess the performance of these five gridded precipitation products. The Standardized Precipitation Index (SPI) is used to quantify drought and wetness. GPCC and APHRODITE performed reasonably well in reproducing spatial and temporal precipitation patterns. GPCC performs consistently better than APHRODITE in all the statistical tests. Except for UDEL, other gridded data sets able to exhibit the characteristics of drought/wetness (e.g. the percentage of drought events and severity) during strong El Nino Southern Oscillation (ENSO) events. However, higher uncertainty exists to quantify drought inter-arrival time in most of the data sets. Furthermore, trend analysis was performed to evaluate the comparative performance of gridded data sets to quantify drought (wet) spells at annual and seasonal time scales. Although the gauge-based and hybrid satellite-gauge merged products use partly ground truth data, the different interpolation techniques and merging algorithms may contribute to large uncertainties.