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Evaluation of gridded precipitation data for driving SWAT model in area upstream of Three Gorges Reservoir.

Yang Y, Wang G, Wang L, Yu J, Xu Z - PLoS ONE (2014)

Bottom Line: The results of one test basin with significant topographic influence indicates that all the gridded data have poor abilities in reproducing hydrologic processes with the topographic influence on precipitation quantity and distribution.However, in a relatively flat test basin, the APHRODITE and trend surface data can give stable and desirable results.The results of this study suggest that precipitation data for future applications should be considered comprehensively in the TGR area, including the influence of data density and topography.

View Article: PubMed Central - PubMed

Affiliation: College of Water Sciences, Beijing Normal University, Beijing 100875, China; United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan.

ABSTRACT
Gridded precipitation data are becoming an important source for driving hydrologic models to achieve stable and valid simulation results in different regions. Thus, evaluating different sources of precipitation data is important for improving the applicability of gridded data. In this study, we used three gridded rainfall datasets: 1) National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR); 2) Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE); and 3) China trend-surface reanalysis (trend surface) data. These are compared with monitoring precipitation data for driving the Soil and Water Assessment Tool in two basins upstream of Three Gorges Reservoir (TGR) in China. The results of one test basin with significant topographic influence indicates that all the gridded data have poor abilities in reproducing hydrologic processes with the topographic influence on precipitation quantity and distribution. However, in a relatively flat test basin, the APHRODITE and trend surface data can give stable and desirable results. The results of this study suggest that precipitation data for future applications should be considered comprehensively in the TGR area, including the influence of data density and topography.

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Annual precipitation value of various precipitation datasets for the Dong River (left) and the Puli River (right) basins.
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pone-0112725-g003: Annual precipitation value of various precipitation datasets for the Dong River (left) and the Puli River (right) basins.

Mentions: The areal distributions of different precipitation datasets are shown in Figure 2 and 3. The annual precipitation distribution of in Dong River basin (Figure 2, left) indicate that the results of monitoring data and the NCEP-CFSR data present more variation compared with the APRHODITE data and the trend surface data. The precipitation value of Dong River basin calculated by the monitoring data is varied from 800 mm to 1600 mm. The value of NCEP-CFSR data indicates a more large variation, which is from 800 mm to 1800 mm. However, the results of APHRODITE data and the trend surface data show that the annual precipitation in Dong River basin is from 1000 mm to 1200 mm. In the Puli River basin, the monitoring precipitation data indicate that the annual precipitation amount is from 800 mm to 1200 mm in Puli River basin (Figure 2, right). As same as in the Dong River basin, the APHRODITE data and the trend surface data also perform similar results in Puli River basin. The range of annual precipitation of these two datasets is from 1100 mm to 1300 mm. Though the NCEP-CFSR also shows the annual precipitation is from 1100 mm to 1300 mm, the spatial distribution clearly indicate that it present contrary results compared with other three datasets. In the downstream of Puli River basin, the annual precipitation calculated from the NCEP-CFSR data is lower than other three datasets. However, in the upstream, the annual precipitation of NCEP-CFSR data present relative higher values compared with other datasets.


Evaluation of gridded precipitation data for driving SWAT model in area upstream of Three Gorges Reservoir.

Yang Y, Wang G, Wang L, Yu J, Xu Z - PLoS ONE (2014)

Annual precipitation value of various precipitation datasets for the Dong River (left) and the Puli River (right) basins.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4237342&req=5

pone-0112725-g003: Annual precipitation value of various precipitation datasets for the Dong River (left) and the Puli River (right) basins.
Mentions: The areal distributions of different precipitation datasets are shown in Figure 2 and 3. The annual precipitation distribution of in Dong River basin (Figure 2, left) indicate that the results of monitoring data and the NCEP-CFSR data present more variation compared with the APRHODITE data and the trend surface data. The precipitation value of Dong River basin calculated by the monitoring data is varied from 800 mm to 1600 mm. The value of NCEP-CFSR data indicates a more large variation, which is from 800 mm to 1800 mm. However, the results of APHRODITE data and the trend surface data show that the annual precipitation in Dong River basin is from 1000 mm to 1200 mm. In the Puli River basin, the monitoring precipitation data indicate that the annual precipitation amount is from 800 mm to 1200 mm in Puli River basin (Figure 2, right). As same as in the Dong River basin, the APHRODITE data and the trend surface data also perform similar results in Puli River basin. The range of annual precipitation of these two datasets is from 1100 mm to 1300 mm. Though the NCEP-CFSR also shows the annual precipitation is from 1100 mm to 1300 mm, the spatial distribution clearly indicate that it present contrary results compared with other three datasets. In the downstream of Puli River basin, the annual precipitation calculated from the NCEP-CFSR data is lower than other three datasets. However, in the upstream, the annual precipitation of NCEP-CFSR data present relative higher values compared with other datasets.

Bottom Line: The results of one test basin with significant topographic influence indicates that all the gridded data have poor abilities in reproducing hydrologic processes with the topographic influence on precipitation quantity and distribution.However, in a relatively flat test basin, the APHRODITE and trend surface data can give stable and desirable results.The results of this study suggest that precipitation data for future applications should be considered comprehensively in the TGR area, including the influence of data density and topography.

View Article: PubMed Central - PubMed

Affiliation: College of Water Sciences, Beijing Normal University, Beijing 100875, China; United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan.

ABSTRACT
Gridded precipitation data are becoming an important source for driving hydrologic models to achieve stable and valid simulation results in different regions. Thus, evaluating different sources of precipitation data is important for improving the applicability of gridded data. In this study, we used three gridded rainfall datasets: 1) National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR); 2) Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE); and 3) China trend-surface reanalysis (trend surface) data. These are compared with monitoring precipitation data for driving the Soil and Water Assessment Tool in two basins upstream of Three Gorges Reservoir (TGR) in China. The results of one test basin with significant topographic influence indicates that all the gridded data have poor abilities in reproducing hydrologic processes with the topographic influence on precipitation quantity and distribution. However, in a relatively flat test basin, the APHRODITE and trend surface data can give stable and desirable results. The results of this study suggest that precipitation data for future applications should be considered comprehensively in the TGR area, including the influence of data density and topography.

Show MeSH