As an important forcing data for hydrologic models, precipitation has significant effects on model simulation. The China Meteorological Forcing Dataset （ITP） and Global Land Data Assimilation System （GLDAS） precip...As an important forcing data for hydrologic models, precipitation has significant effects on model simulation. The China Meteorological Forcing Dataset （ITP） and Global Land Data Assimilation System （GLDAS） precipitation data are the two commonly used data sources in the Heihe River Basin （HRB）. This paper focused on evaluating the accuracy of these two precipitation datasets. A set of metrics were developed to characterize the trend, magnitude, annual allocation, event matching, frequency, and spatial distribution of the two datasets. Meanwhile, such accuracy evaluation was performed at various scales, i.e., daily, monthly, and yearly. By comparing with observations, this study concluded that： first, both ITP and GLDAS precipitation data well represented the trends at corresponding sites, and GLDAS underestimated precipita- tion in most regions except the east tributary headwater region; second, unusual annual precipitation distribution was observed in both datasets with overestimation of precipitation in May through September and GLDAS appeared to be much severe; third, the ITP data seriously over-predicted the precipitation events; fourth, the ITP data have better spatial distribution than GLDAS in the upper reach area of HRB. Overall, we recommended ITP precipitation data for the land surface study in the uooer reach of HRB.展开更多
基金supported by NSFC (91125006)partially by state key laboratory grant (SKLFSE201009)
文摘As an important forcing data for hydrologic models, precipitation has significant effects on model simulation. The China Meteorological Forcing Dataset （ITP） and Global Land Data Assimilation System （GLDAS） precipitation data are the two commonly used data sources in the Heihe River Basin （HRB）. This paper focused on evaluating the accuracy of these two precipitation datasets. A set of metrics were developed to characterize the trend, magnitude, annual allocation, event matching, frequency, and spatial distribution of the two datasets. Meanwhile, such accuracy evaluation was performed at various scales, i.e., daily, monthly, and yearly. By comparing with observations, this study concluded that： first, both ITP and GLDAS precipitation data well represented the trends at corresponding sites, and GLDAS underestimated precipita- tion in most regions except the east tributary headwater region; second, unusual annual precipitation distribution was observed in both datasets with overestimation of precipitation in May through September and GLDAS appeared to be much severe; third, the ITP data seriously over-predicted the precipitation events; fourth, the ITP data have better spatial distribution than GLDAS in the upper reach area of HRB. Overall, we recommended ITP precipitation data for the land surface study in the uooer reach of HRB.
