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Application of SWAT99.2 to sensitivity analysis of water balance components in unique plots in a hilly region
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作者 Jun-feng Dai Jia-zhou chen +3 位作者 Guo-an Lu larry c. brown Lei Gan Qin-xue Xu 《Water Science and Engineering》 EI CAS CSCD 2017年第3期209-216,共8页
Although many sensitivity analyses using the soil and water assessment tool(SWAT) in a complex watershed have been conducted, little attention has been paid to the application potential of the model in unique plots. I... Although many sensitivity analyses using the soil and water assessment tool(SWAT) in a complex watershed have been conducted, little attention has been paid to the application potential of the model in unique plots. In addition, sensitivity analysis of percolation and evapotranspiration with SWAT has seldom been undertaken. In this study, SWAT99.2 was calibrated to simulate water balance components for unique plots in Southern China from 2000 to 2001, which included surface runoff, percolation, and evapotranspiration. Twenty-one parameters classified into four categories, including meteorological conditions, topographical characteristics, soil properties, and vegetation attributes, were used for sensitivity analysis through one-at-a-time(OAT) sampling to identify the factor that contributed most to the variance in water balance components. The results were shown to be different for different plots, with parameter sensitivity indices and ranks varying for different water balance components. Water balance components in the broad-leaved forest and natural grass plots were most sensitive to meteorological conditions, less sensitive to vegetation attributes and soil properties, and least sensitive to topographical characteristics. Compared to those in the natural grass plot, water balance components in the broad-leaved forest plot demonstrated higher sensitivity to the maximum stomatal conductance(GSI) and maximum leaf area index(BLAI). 展开更多
关键词 Forest and GRASS PLOTS WATER balance Sensitivity analysis Soil and WATER assessment tool (SWAT) One-at-a-time (OAT) method
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